CN107887933A - A kind of Multiple Time Scales rolling optimization microgrid energy optimum management method - Google Patents

A kind of Multiple Time Scales rolling optimization microgrid energy optimum management method Download PDF

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CN107887933A
CN107887933A CN201711145966.4A CN201711145966A CN107887933A CN 107887933 A CN107887933 A CN 107887933A CN 201711145966 A CN201711145966 A CN 201711145966A CN 107887933 A CN107887933 A CN 107887933A
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窦春霞
王学伟
王瑞山
郑宇航
罗维
张占强
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Yanshan University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H02J3/382—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a kind of Multiple Time Scales rolling optimization microgrid energy optimum management method, two time scales are divided first, in a few days optimize with operating cost and the minimum target of system losses, using each distributed power source start-stop, run-limiting as constraint, while consider that three kinds of load responding costs and constraint founding mathematical models are in a few days optimized.Real-time optimization is using in a few days optimum results as reference quantity, performance model predictive control algorithm, and the rolling optimization carried out in following finite time-domain solves.The inventive method can consider that each distributed power source constraint limitation, various part throttle characteristics take into account power attenuation to ensure micro-capacitance sensor safe and stable operation comprehensively, improve the economy of microgrid operation.

Description

A kind of Multiple Time Scales rolling optimization microgrid energy optimum management method
Technical field
The present invention relates to generation of electricity by new energy and the technical field of intelligent grid, especially it is a kind of consider demand response it is more when Between yardstick rolling optimization microgrid energy optimum management method.
Background technology
With gradually stepping up for regenerative resource permeability, distributed power generation gradually rises in various countries, but distributed hair Electricity has dispersiveness, not manageability, therefore just have the appearance of micro-capacitance sensor.Micro-capacitance sensor be it is a kind of by distributed power source such as photoelectricity, Wind-powered electricity generation, miniature gas turbine, energy-storage system, and the small grids of load composition.Various distributed power sources have each self-operating Advantage and disadvantage, especially regenerative resource power supply intermittence, it is necessary to microgrid energy Optimal Management System to various distributed power sources Coordinated and optimized, to ensure that micro-grid system can economic, safe and reliable operation.
Energy-optimised management for micro-capacitance sensor is now with many researchs, but existing research is present to the consideration of microgrid structure not Comprehensively, renewable energy power generation is constrained it is inconsiderate, not congruent problem is considered to operating cost.Relevant scholar proposes to consider to need The method of the microgrid energy optimum management of side is sought, but only considered two kinds of loads, respectively controllable and uncontrollable load, and do not have Have and careful division is carried out to controllable burden, power can not directly be cut down by not accounting for some loads and can only translating.Also do not examine Some loads are considered once the constraint that can not stop.Relevant scholar proposes the optimization method of Multiple Time Scales, but only one Subdivided time scale, be divided into a few days ago, in a few days, real-time three periods, without handling the controlled distribution formula power supply that issues in advance Contributing between actual active power output, there is the problem of deviation.It would therefore be highly desirable to propose one kind to each distributed power source and load Performance, species consideration are thorough, and can effectively handle and issue controlled distribution formula power supply output and deviation between actual active power output in advance The optimization method of problem.
The content of the invention
Present invention aims at provide one kind to consider each distributed power source constraint limitation, various part throttle characteristics to ensure comprehensively Micro-capacitance sensor safe and stable operation while power attenuation is taken into account, so that micro-capacitance sensor operation can reach the purpose, as far as possible of economy Eliminate the advanced Multiple Time Scales rolling optimization micro-capacitance sensor energy for issuing controlled distribution formula power supply output and actual active power output deviation Measure optimum management method.
To achieve the above object, following technical scheme is employed：The specific construction step of optimization method of the present invention is such as Under：
Step 1, long time scale optimization is segmented by the hour, is divided into 24 periods by one day；
Step 2, the mathematical modeling of each distributed power source and load is established；
Step 3, microgrid energy optimum management in a few days optimized mathematical model is established；
Step 4, with mathematical modulo pattern in particle swarm optimization algorithm step 2,3, obtaining controlled distribution formula power supply has Work(output PDGg(t), power P is interacted with bulk power gridgrid(t), energy storage output Pbat(t) and can cutting load power resection rate ρcli(t) And deferrable load running status δalj(t) planned value；
Step 5, real-time optimization was divided into 96 periods with 15 minutes for time interval by one day；
Step 6, real-time optimization mathematical modeling is established；
Step 7, the real-time optimization mathematical modulo pattern in solution procedure 6, predicts each distributed power source active power output, with big electricity Net interaction power and the power adjustment that distributed power source can be cut；
Step 8, each distributed power source active power output that will be predicted, power is interacted with bulk power grid and distributed power source can be cut Power adjustment with reality measured value compared with, form with feedback closed-loop control system；
Step 9, each distributed power source active power output is exported, power is interacted with bulk power grid and the power of distributed power source can be cut The optimal result of adjustment amount.
Further, the mathematical modeling of each distributed power source and load established in step 2 is as follows：
Step 2.1：The mathematical modeling of controlled distribution formula power supply
For controlled distribution formula power supply, its operating cost is mainly operation and maintenance cost and fuel cost, is run into This function such as formula (1)：
CDGg(t)=a [PDGg(t)]2+bPDGg(t)+cδDGg(t) (1)
In formula, a, b, c be operating cost function quadratic term, first order and constant term coefficient, δDGg(t) it is distributed electrical The start-stop state variable in source；And the constraint to controlled distribution formula power supply mainly consider power output constraint, the constraint of operation climbing rate, Minimum run time and the constraint of minimum dwell time.
|PDGg(t)-PDGg(t-1)|≤ΔPDGg (4)
Formula (2) represents the active and reactive power and line impedance and the relation of node voltage of distributed power source；
Formula (3) is constrained for the power output of distributed power source, and controlled distribution formula power supply must be run within the specific limits；Formula (4) it is operation climbing rate constraint, Δ PDGgFor changed power of the distributed power source within the Δ t times；
Formula (5) constrains for the minimum run time constraint of controlled distribution formula power supply and minimum downtime, τ1And τ2Respectively Distributed power source minimum run time and the auxiliary variable of constraint of minimum downtime.
Step 2.2：Micro-capacitance sensor and bulk power grid interaction models
Micro-capacitance sensor is relevant with electricity price with the interaction cost of bulk power grid, and cost function is shown below：
Cgrid=cgrid(t)Pgrid(t) (6)
In formula (7), cgrid(t) the price valency of dealing electricity is represented；Formula (8) is the active reactive that micro-capacitance sensor interacts with bulk power grid Power constraint；
Step 2.3：Establish energy storage model
Energy-storage battery can all be used for the life-span in charge and discharge process and impact, be given below the use of energy-storage battery into This function and power, capacity-constrained.
In formula, λbatFor the scheduling cost coefficient of energy-storage battery, PbatAnd E (t)bat(t) be respectively battery charge and discharge electric work Rate and capacity.
Step 2.4：Load model
Micro-grid load is divided into three classes：One kind is uncontrollable critical loads, and system must meet such load first Power demand；For second class for that can cut load, there is certain elastic range in the operation power of this kind of load, can be according to electricity price machine System carries out power adjustment；3rd class is deferrable load, and the working hour of the type load has regular hour scope, can basis Tou power price controls its working time.
Formula (11) constrains for the active and reactive power of critical load；
Formula (12) for can cutting load power adjustment constraint,WithFor can maximum, the most I of cutting load cut rate；
Formula (13) is constrained for the operation of deferrable load, and deferrable load is only run in preset time window, and is once transported Row can not stop before the task of not completing.
Ccli(t)=εcli(t)ρcli(t)Pcli(t) (14)
Formula (14) for excision can cutting load power network compensation expense, εcli(t) for can cutting load scheduling cost coefficient,For load i rated power；
Formula (15) is the load adjustment expense of deferrable load；
Further, the mathematical modeling that the microgrid energy optimum management established in step 3 in a few days optimizes is as follows：
It is assumed that on the premise of voltage stabilization, with the total operating cost of micro-capacitance sensor and the minimum target of system losses, target letter Number and active reactive equality constraint are as follows：
Object function：
MinF=min (F1,F2) (16)
(17) formula is the total operation and maintenance cost function of micro-capacitance sensor, CDGg(t) it is the operation maintenance expense of controlled distribution formula power supply With；cgrid(t) it is tou power price Pgrid(t) active power to be interacted with bulk power grid；Cbat(t) it is the use cost of battery； Ccli(t) for can cutting load excision reimbursement for expenses；Cali(t) it is the load adjustment reimbursement for expenses of adjustable load；
(18) formula is the system active power loss function of micro-capacitance sensor.
Constraints：
(19), (20) formula is respectively the active equality constraint of micro-capacitance sensor operation and idle equality constraint.Wherein PDGg(t) and QDGg(t) be respectively distributed power source g active power and reactive power, NR is the number of controlled distribution formula power supply, and R is can be again The number of the raw energy；Qgrid(t) reactive power to be interacted with bulk power grid；PcliAnd Q (t)cli(t) it is respectively load curtailment i Active and reactive power, I be load curtailment quantity；PaljAnd Q (t)alj(t) be respectively the active of adjustable load j and Reactive power, J are the quantity of adjustable load；PnlkAnd Q (t)nlk(t) be respectively uncontrollable load k active and reactive power, S is the quantity of uncontrollable load；PlossAnd Q (t)loss(t) be respectively micro-capacitance sensor active and reactive power loss.
In formula, GabFor the conductance between the node of a, b two, θabFor the phase angle difference between the node of a, b two, n is system node number.
Further, the real-time optimization mathematical modeling established in step 6 is as follows：
Using the rolling forecast value of photovoltaic and wind power generation and critical load as input variable, with controllable point in active distribution network The actual measuring value of cloth power supply is as initial value P0(k+n), increased with the active power output of distributed power source in following finite time-domain Amount, interact with bulk power grid power change values and can cut the power adjustment variable quantity of distributed power source be to control variable Δ u (k+t- 1), real-time optimization is with the minimum target of active power output deviation, and being limited to constraint with each several part active power output, to establish model as follows：
Pmin(k+n)≤P(k+n)≤Pmax(k+n) (26)
In formula, Pr(k+n) it is active power output reference value, by a few days optimizing to obtain；P (k+n) is the distribution of real-time optimization Power supply, bulk power grid, energy storage and can cutting load active power output predicted value；P0(k+n) it is real-time optimization each several part active power output Initial value, obtained by actual measurement；Δ u (k+t-1) is the active power output increment of [k+t-1, the k+t] of prediction in the period, is excellent The control variable of change；δalj(k+n) it is the control variable of deferrable load, should also meets in addition to it need to meet formula (29) constraint in a few days excellent Change the constraint of Chinese style (13).
Compared with prior art, the invention has the advantages that：
1st, many researchs all only considered Generation Side when energy-optimised management is done, and the present invention is considering Generation Side The research to user side is added simultaneously, improves the economy of microgrid operation.
2nd, some researchs take into account Demand-side, but load simply only is divided into controllable and uncontrollable two class, not fill Divide and consider part throttle characteristics, the present invention fully have studied part throttle characteristics, load is divided into three classes, has effectively accomplished peak load shifting, drop The low operating cost of microgrid.
3rd, the present invention uses the optimization method of Multiple Time Scales, has effectively handled the interval of renewable energy power generation and load Property and fluctuation sex chromosome mosaicism.
4th, the present invention has reached more preferable excellent in real-time optimization using the Model Predictive Control Algorithm with closed-loop control Change effect.
The line loss problem that most of researchs do not account for, and line power constraint are also contemplated when the 5, establishing model, The running status of micro-capacitance sensor is handled more comprehensive.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is the structure chart of micro-capacitance sensor of the present invention.
Fig. 3 is the graph of a relation of long time scale of the present invention and short-term time scale.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings：
Because microgrid is complicated, comprising individual kind of distributed power source and load, and each distributed power source and load have each Operation characteristic, the present invention initially set up a micro-capacitance sensor structure as shown in Fig. 2 again due to renewable energy power generation and load The characteristics of with intermittent and fluctuation, and its precision of prediction is relevant with time scale, therefore the present invention will be from two time chis Degree optimizes, and divides two time scales first, the relation of two time scales is as shown in figure 3, then in two time scales Lower step-by-step optimization, specific steps are as shown in Figure 1：
Step 1, in a few days optimize and be segmented by the hour, be divided into 24 periods by one day, once light in following 1h is predicted per 15min Volt, the power output of blower fan, the power demand of each load and the state-of-charge of energy-storage battery；
Step 2, the mathematical modeling of each distributed power source and load is established；
Step 2.1：Establish the mathematical modeling of controlled distribution formula power supply
For controlled distribution formula power supply, its operating cost is mainly operation and maintenance cost and fuel cost, is run into This function such as formula (1).
CDGg(t)=a [PDGg(t)]2+bPDGg(t)+cδDGg(t) (1)
A in formula, b, c be operating cost function quadratic term, first order and constant term coefficient, δDGg(t) it is distributed power source Start-stop state variable.And the constraint to controlled distribution formula power supply mainly considers that power output constraint, operation climbing rate constrain, most Small run time and the constraint of minimum dwell time.
|PDGg(t)-PDGg(t-1)|≤ΔPDGg (4)
Formula (2) represents the active and reactive power and line impedance and the relation of node voltage of distributed power source；Formula (3) is The power output constraint of distributed power source, controlled distribution formula power supply must be run within the specific limits；Formula (4) is to run climbing rate about Beam, Δ PDGgFor changed power of the distributed power source within the Δ t times；Formula (5) is the minimum run time of controlled distribution formula power supply Constraint and the constraint of minimum downtime, τ1And τ2Respectively distributed power source minimum run time and minimum downtime constrain Auxiliary variable.
Step 2.2：Establish micro-capacitance sensor and bulk power grid interaction models
Micro-capacitance sensor is relevant with electricity price with the interaction cost of bulk power grid, and cost function is shown below：
Cgrid=cgrid(t)Pgrid(t) (6)
Formula (8) is the active reactive power constraint that micro-capacitance sensor interacts with bulk power grid.
Step 2.3：Establish energy storage model
Energy-storage battery can all be used for the life-span in charge and discharge process and impact, be given below the use of energy-storage battery into This function and power, capacity-constrained.
In formula, λbatFor the scheduling cost coefficient of energy-storage battery, PbatAnd E (t)bat(t) be respectively battery charge and discharge electric work Rate and capacity.
Step 2.4：Establish load model
The micro-grid load considered herein is divided into three classes：One kind is uncontrollable critical loads, and system must meet first The power demand of such load；For second class can to cut load, there is certain elastic range in the operation power of this kind of load, can be with Power adjustment is carried out according to Price Mechanisms；3rd class is deferrable load, and the working hour of the type load has regular hour model Enclose, its working time can be controlled according to tou power price.
Formula (11) constrains for the active and reactive power of critical load.Formula (12) for can cutting load power adjustment constraint,WithFor can maximum, the most I of cutting load cut rate.Formula (13) constrains for the operation of deferrable load, and deferrable load only exists Run in preset time window, and can not stop before once operating in unfinished task.
Ccli(t)=εcli(t)ρcli(t)Pcli(t) (14)
Formula (14) for excision can cutting load power network compensation expense, εcli(t) for can cutting load scheduling cost coefficient,For load i rated power；Formula (15) is the load adjustment expense of deferrable load.
Step 3, energy-optimised Management Mathematics model is established；
It is assumed that on the premise of voltage stabilization, with the total operating cost of micro-capacitance sensor and the minimum target of system losses, target letter Number and active reactive equality constraint are as follows：
Object function：
MinF=min (F1,F2) (16)
Formula (17) is the total operation and maintenance cost function of micro-capacitance sensor, CDGg(t) it is the operation maintenance expense of controlled distribution formula power supply With；cgrid(t) it is tou power price Pgrid(t) active power to be interacted with bulk power grid；Cbat(t) it is the use cost of battery； Ccli(t) for can cutting load excision reimbursement for expenses；Cali(t) it is the load adjustment reimbursement for expenses of adjustable load.Formula (18) is The system active power loss function of micro-capacitance sensor.
Constraints：
Formula (19), (20) are respectively the active equality constraint of micro-capacitance sensor operation and idle equality constraint.Wherein PDGg(t) and QDGg(t) be respectively distributed power source g active power and reactive power, NR is the number of controlled distribution formula power supply, and R is can be again The number of the raw energy.Qgrid(t) reactive power to be interacted with bulk power grid.PcliAnd Q (t)cli(t) it is respectively load curtailment i Active and reactive power, I be load curtailment quantity.PaljAnd Q (t)alj(t) be respectively the active of adjustable load j and Reactive power, J are the quantity of adjustable load.PnlkAnd Q (t)nlk(t) be respectively uncontrollable load k active and reactive power, S is the quantity of uncontrollable load.PlossAnd Q (t)loss(t) be respectively micro-capacitance sensor active and reactive power loss.
G in formulaabFor the conductance between the node of a, b two, θabFor the phase angle difference between the node of a, b two, n is system node number.
Step 4, with particle swarm optimization algorithm mathematical modulo pattern (1)-(22), obtaining controlled distribution formula power supply has Work(output PDGg(t), power P is interacted with bulk power gridgrid(t), energy storage output Pbat(t) and can cutting load power resection rate ρcli(t) And deferrable load running status δalj(t) planned value.
Step 5, real-time optimization was divided into 96 periods using 15min as time interval by one day, was predicted once not per 5min The state-of-charge of the photovoltaic, the power output of blower fan, the power demand of each load and the energy-storage battery that come in 15min；
Step 6, real-time optimization mathematical modeling is established；
Using the rolling forecast value of photovoltaic and wind power generation and critical load as input variable, with controllable point in active distribution network The actual measuring value of cloth power supply is as initial value P0(k+n), increased with the active power output of distributed power source in following finite time-domain Amount, interact with bulk power grid power change values and can cut the power adjustment variable quantity of distributed power source be to control variable Δ u (k+t- 1), short-term time scale is with the minimum target of active power output deviation, and being limited to constraint with each several part active power output, to establish model as follows：
Pmin(k+n)≤P(k+n)≤Pmax(k+n) (26)
In formula, Pr(k+n) it is active power output reference value, by a few days optimizing to obtain；P (k+n) is the distribution of real-time optimization Power supply, bulk power grid, energy storage and can cutting load active power output predicted value；P0(k+n) it is real-time optimization each several part active power output Initial value, obtained by actual measurement；Δ u (k+t-1) is the active power output increment of [k+t-1, the k+t] of prediction in the period, is excellent The control variable of change.δalj(k+n) it is the control variable of deferrable load, should also meets in addition to it need to meet formula (29) constraint in a few days excellent Change the constraint of Chinese style (13).
Step 7, real-time optimization mathematical modulo pattern (23)-(29) are solved, predict each distributed power source active power output, it is and big Power network interacts power and can cut the power adjustment of distributed power source.
Step 8, each distributed power source active power output that will be predicted, power is interacted with bulk power grid and distributed power source can be cut Power adjustment with reality measured value compared with, form with feedback closed-loop control system.
Step 9, each distributed power source active power output is exported, power is interacted with bulk power grid and the power of distributed power source can be cut The optimal result of adjustment amount.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention Enclose and be defined, on the premise of design spirit of the present invention is not departed from, technical side of the those of ordinary skill in the art to the present invention The various modifications and improvement that case is made, it all should fall into the protection domain of claims of the present invention determination.

Claims (4)

  1. A kind of 1. Multiple Time Scales rolling optimization microgrid energy optimum management method, it is characterised in that methods described is specifically interior Hold as follows：
    Step 1, in a few days optimize and be segmented by the hour, be divided into 24 periods by one day；
    Step 2, the mathematical modeling of each distributed power source and load is established；
    Step 3, microgrid energy optimum management in a few days optimized mathematical model is established；
    Step 4, with mathematical modulo pattern in particle swarm optimization algorithm step 2,3, obtain controlled distribution formula power supply it is active go out Power PDGg(t), power P is interacted with bulk power gridgrid(t), energy storage output Pbat(t) and can cutting load power resection rate ρcli(t) and can Adjust load operating region δalj(t) planned value；
    Step 5, real-time optimization was divided into 96 periods with 15 minutes for time interval by one day；
    Step 6, real-time optimization mathematical modeling is established；
    Step 7, real-time optimization mathematical modulo pattern in solution procedure 6, predicts each distributed power source active power output, is interacted with bulk power grid Power and the power adjustment that distributed power source can be cut；
    Step 8, each distributed power source active power output that will be predicted, power is interacted with bulk power grid and the work(of distributed power source can be cut Rate adjustment amount forms the closed-loop control system with feedback compared with the measured value of reality；
    Step 9, each distributed power source active power output is exported, power is interacted with bulk power grid and the power adjustment of distributed power source can be cut The optimal result of amount.
  2. 2. a kind of Multiple Time Scales rolling optimization microgrid energy optimum management method according to claim 1, its feature It is, the mathematical modeling of each distributed power source and load established in step 2 is as follows：
    Step 2.1：The mathematical modeling of controlled distribution formula power supply
    For controlled distribution formula power supply, its operating cost is mainly operation and maintenance cost and fuel cost, operating cost letter Number is such as formula (1)；
    CDGg(t)=a [PDGg(t)]2+bPDGg(t)+cδDGg(t) (1)
    In formula, a, b, c be operating cost function quadratic term, first order and constant term coefficient, δDGg(t) it is distributed power source Start-stop state variable；And the constraint to controlled distribution formula power supply mainly considers power output constraint, the constraint of operation climbing rate, minimum Run time and the constraint of minimum dwell time；
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>P</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <msubsup> <mi>R</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>X</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mn>2</mn> </msubsup> </mrow> </mfrac> <mo>&amp;lsqb;</mo> <msub> <mi>R</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mo>-</mo> <mi>E</mi> <mi> </mi> <msub> <mi>cos&amp;theta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>+</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>X</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mi>E</mi> <mi> </mi> <msub> <mi>sin&amp;theta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>Q</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <msubsup> <mi>R</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>X</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mn>2</mn> </msubsup> </mrow> </mfrac> <mo>&amp;lsqb;</mo> <msub> <mi>X</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>V</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mo>-</mo> <mi>E</mi> <mi> </mi> <msub> <mi>cos&amp;theta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <msub> <mi>R</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mi>E</mi> <mi> </mi> <msub> <mi>sin&amp;theta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mi>min</mi> </msubsup> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mi>max</mi> </msubsup> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>Q</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mi>min</mi> </msubsup> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>Q</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>Q</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> <mi>max</mi> </msubsup> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
    |PDGg(t)-PDGg(t-1)|≤ΔPDGg (4)
    <mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;tau;</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;tau;</mi> <mn>1</mn> </msub> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>min</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <msup> <mi>T</mi> <mrow> <mi>u</mi> <mi>p</mi> </mrow> </msup> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>T</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> <mo>;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>-</mo> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>&amp;delta;</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>&amp;tau;</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;tau;</mi> <mn>2</mn> </msub> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mi>t</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>min</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>+</mo> <msup> <mi>T</mi> <mrow> <mi>d</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> </mrow> </msup> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>T</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
    Formula (2) represents the active and reactive power and line impedance and the relation of node voltage of distributed power source；
    Formula (3) is constrained for the power output of distributed power source, and controlled distribution formula power supply must be run within the specific limits；
    Formula (4) constrains for operation climbing rate, Δ PDGgFor changed power of the distributed power source within the Δ t times；
    Formula (5) constrains for the minimum run time constraint of controlled distribution formula power supply and minimum downtime, τ1And τ2Respectively it is distributed Formula power supply minimum run time and the auxiliary variable of constraint of minimum downtime；
    Step 2.2：Micro-capacitance sensor and bulk power grid interaction models
    Micro-capacitance sensor is relevant with electricity price with the interaction cost of bulk power grid, and cost function is shown below：
    Cgrid=cgrid(t)Pgrid(t) (6)
    <mrow> <msub> <mi>c</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>c</mi> <mrow> <mi>b</mi> <mi>u</mi> <mi>y</mi> </mrow> </msub> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>c</mi> <mrow> <mi>s</mi> <mi>e</mi> <mi>l</mi> <mi>l</mi> </mrow> </msub> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mo>&lt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mo>-</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>max</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>max</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msubsup> <mi>Q</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>max</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>Q</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>Q</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>max</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>8</mn> <mo>)</mo> </mrow> </mrow>
    In formula (7), cgrid(t) the price valency of dealing electricity is represented；Formula (8) is the active reactive power that micro-capacitance sensor interacts with bulk power grid Constraint；
    Step 2.3：Establish energy storage model
    The use cost function and power of energy-storage battery, capacity-constrained；
    <mrow> <msub> <mi>C</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>&amp;lambda;</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <msubsup> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mn>2</mn> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>max</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>max</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
    In formula, λbatFor the scheduling cost coefficient of energy-storage battery, PbatAnd E (t)bat(t) be respectively battery charge-discharge electric power and Capacity；
    Step 2.4：Load model
    Micro-grid load is divided into three classes：One kind is uncontrollable critical loads, and system must meet the power of such load first Demand；For second class for that can cut load, there is certain elastic range, can enter according to Price Mechanisms in the operation power of this kind of load Row power adjusts；3rd class is deferrable load, and the working hour of the type load has regular hour scope, can be according to timesharing Electricity price controls its working time；
    <mrow> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>P</mi> <mrow> <mi>n</mi> <mi>l</mi> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>P</mi> <mrow> <mi>n</mi> <mi>l</mi> <mi>k</mi> </mrow> <mi>max</mi> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <msub> <mi>Q</mi> <mrow> <mi>n</mi> <mi>l</mi> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>Q</mi> <mrow> <mi>n</mi> <mi>l</mi> <mi>k</mi> </mrow> <mi>max</mi> </msubsup> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msubsup> <mi>&amp;rho;</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>&amp;rho;</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>t</mi> <mi>j</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>t</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <msub> <mi>T</mi> <mrow> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>r</mi> <mi>t</mi> <mi>j</mi> </mrow> </msub> </mrow> <msub> <mi>T</mi> <mrow> <mi>e</mi> <mi>n</mi> <mi>d</mi> <mi>j</mi> </mrow> </msub> </munderover> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>13</mn> <mo>)</mo> </mrow> </mrow>
    Formula (11) constrains for the active and reactive power of critical load；
    Formula (12) for can cutting load power adjustment constraint,WithFor can maximum, the most I of cutting load cut rate；
    Formula (13) is constrained for the operation of deferrable load, and deferrable load is only run in preset time window, and is once operated in It can not stop before the task of not completing；
    Ccli(t)=εcli(t)ρcli(t)Pcli(t) (14)
    <mrow> <msub> <mi>C</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <mn>24</mn> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>c</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>P</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> <mi>T</mi> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>15</mn> <mo>)</mo> </mrow> </mrow>
    Formula (14) for excision can cutting load power network compensation expense, εcli(t) for can cutting load scheduling cost coefficient,For Load i rated power；
    Formula (15) is the load adjustment expense of deferrable load.
  3. 3. a kind of Multiple Time Scales rolling optimization microgrid energy optimum management method according to claim 1, its feature It is, the mathematical modeling that the microgrid energy optimum management established in step 3 in a few days optimizes is as follows：
    It is assumed that on the premise of voltage stabilization, with the total operating cost of micro-capacitance sensor and the minimum target of system losses, object function and Active reactive equality constraint is as follows：
    Object function：
    MinF=min (F1,F2) (16)
    <mrow> <mtable> <mtr> <mtd> <mrow> <msub> <mi>F</mi> <mn>1</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>g</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>N</mi> <mi>R</mi> </mrow> </munderover> <msub> <mi>C</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <msub> <mi>c</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <msub> <mi>C</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msub> <mi>C</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>J</mi> </munderover> <msub> <mi>C</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>17</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mi>F</mi> <mn>2</mn> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>T</mi> </munderover> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>18</mn> <mo>)</mo> </mrow> </mrow>
    (17) formula is the total operation and maintenance cost function of micro-capacitance sensor, CDGg(t) it is the operation and maintenance cost of controlled distribution formula power supply； cgrid(t) it is tou power price Pgrid(t) active power to be interacted with bulk power grid；Cbat(t) it is the use cost of battery；Ccli (t) for can cutting load excision reimbursement for expenses；Cali(t) it is the load adjustment reimbursement for expenses of adjustable load；
    (18) formula is the system active power loss function of micro-capacitance sensor；
    Constraints：
    <mrow> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>g</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>N</mi> <mi>R</mi> <mo>+</mo> <mi>R</mi> </mrow> </munderover> <msub> <mi>P</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>J</mi> </munderover> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>P</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>S</mi> </munderover> <msub> <mi>P</mi> <mrow> <mi>n</mi> <mi>l</mi> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>19</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>g</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>N</mi> <mi>R</mi> <mo>+</mo> <mi>R</mi> </mrow> </munderover> <msub> <mi>Q</mi> <mrow> <mi>D</mi> <mi>G</mi> <mi>g</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>Q</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>I</mi> </munderover> <msub> <mi>Q</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;lsqb;</mo> <mn>1</mn> <mo>-</mo> <msub> <mi>&amp;rho;</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>J</mi> </munderover> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <msub> <mi>Q</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>S</mi> </munderover> <msub> <mi>Q</mi> <mrow> <mi>n</mi> <mi>l</mi> <mi>k</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>Q</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>Q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>20</mn> <mo>)</mo> </mrow> </mrow>
    (19), (20) formula is respectively the active equality constraint of micro-capacitance sensor operation and idle equality constraint；
    Wherein PDGgAnd Q (t)DGg(t) be respectively distributed power source g active power and reactive power, NR be controlled distribution formula electricity The number in source, R are the number of regenerative resource；Qgrid(t) reactive power to be interacted with bulk power grid；PcliAnd Q (t)cli(t) divide Not Wei load curtailment i active and reactive power, I be load curtailment quantity；PaljAnd Q (t)alj(t) it is respectively adjustable Whole load j active and reactive power, J are the quantity of adjustable load；PnlkAnd Q (t)nlk(t) it is respectively uncontrollable load k Active and reactive power, S are the quantity of uncontrollable load；PlossAnd Q (t)loss(t) be respectively micro-capacitance sensor active and idle work( Rate is lost；
    <mrow> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.5</mn> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>G</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mrow> <mo>(</mo> <msubsup> <mi>V</mi> <mi>a</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>V</mi> <mi>b</mi> <mn>2</mn> </msubsup> <mo>-</mo> <mn>2</mn> <msub> <mi>V</mi> <mi>a</mi> </msub> <msub> <mi>V</mi> <mi>b</mi> </msub> <msub> <mi>cos&amp;theta;</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>21</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mi>Q</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0.5</mn> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>b</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>G</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mrow> <mo>(</mo> <msubsup> <mi>V</mi> <mi>a</mi> <mn>2</mn> </msubsup> <mo>+</mo> <msubsup> <mi>V</mi> <mi>b</mi> <mn>2</mn> </msubsup> <mo>+</mo> <mn>2</mn> <msub> <mi>V</mi> <mi>a</mi> </msub> <msub> <mi>V</mi> <mi>b</mi> </msub> <msub> <mi>sin&amp;theta;</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>22</mn> <mo>)</mo> </mrow> </mrow>
    In formula, GabFor the conductance between the node of a, b two, θabFor the phase angle difference between the node of a, b two, n is system node number.
  4. 4. a kind of Multiple Time Scales rolling optimization microgrid energy optimum management method according to claim 1, its feature It is, the real-time optimization mathematical modeling established in step 6 is as follows：
    Using the rolling forecast value of photovoltaic and wind power generation and critical load as input variable, with controlled distribution formula in active distribution network The actual measuring value of power supply is as initial value P0(k+n), with the active power output increment of distributed power source in following finite time-domain, with Bulk power grid interaction power change values and the power adjustment variable quantity that can cut distributed power source are control variable Δ u (k+t-1), in real time Optimization with the minimum target of active power output deviation, with each several part active power output is limited to constraint, and to establish model as follows：
    <mrow> <mi>min</mi> <mi>J</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msup> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>P</mi> <mi>r</mi> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>P</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>23</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mi>P</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <msub> <mi>P</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>t</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mi>&amp;Delta;</mi> <mi>u</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>t</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>24</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mtable> <mtr> <mtd> <mrow> <msup> <mi>P</mi> <mi>r</mi> </msup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>&amp;lsqb;</mo> <msubsup> <mi>P</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> <mi>r</mi> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>,</mo> <msubsup> <mi>&amp;Delta;P</mi> <mrow> <mi>c</mi> <mi>l</mi> </mrow> <mi>r</mi> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>P</mi> <mrow> <mi>a</mi> <mi>l</mi> </mrow> <mi>r</mi> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>,</mo> <msubsup> <mi>P</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>r</mi> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>,</mo> <msubsup> <mi>P</mi> <mrow> <mi>D</mi> <mi>G</mi> </mrow> <mi>r</mi> </msubsup> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>&amp;rsqb;</mo> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>25</mn> <mo>)</mo> </mrow> </mrow>
    Pmin(k+n)≤P(k+n)≤Pmax(k+n) (26)
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>min</mi> </msubsup> <mo>&amp;le;</mo> <mi>E</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>&amp;le;</mo> <msubsup> <mi>E</mi> <mrow> <mi>b</mi> <mi>a</mi> <mi>t</mi> </mrow> <mi>max</mi> </msubsup> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>27</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <mtable> <mtr> <mtd> <mrow> <mo>&amp;Sigma;</mo> <mi>P</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>&amp;Sigma;</mo> <msub> <mi>P</mi> <mrow> <mi>n</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <mo>&amp;Sigma;</mo> <msub> <mi>P</mi> <mrow> <mi>c</mi> <mi>l</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>J</mi> </munderover> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <msub> <mi>P</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>P</mi> <mrow> <mi>l</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> </mrow> </mtd> </mtr> </mtable> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>28</mn> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mi>&amp;delta;</mi> <mrow> <mi>a</mi> <mi>l</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>c</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>&gt;</mo> <msub> <mi>c</mi> <mi>v</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>c</mi> <mrow> <mi>g</mi> <mi>r</mi> <mi>i</mi> <mi>d</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>k</mi> <mo>+</mo> <mi>n</mi> <mo>)</mo> </mrow> <mo>&lt;</mo> <msub> <mi>c</mi> <mi>v</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>29</mn> <mo>)</mo> </mrow> </mrow>
    In formula, Pr(k+n) it is active power output reference value, by a few days optimizing to obtain；P (k+n) be real-time optimization distributed power source, Bulk power grid, energy storage and can cutting load active power output predicted value；P0(k+n) it is the initial of real-time optimization each several part active power output Value, obtained by actual measurement；Δ u (k+t-1) is the active power output increment of [k+t-1, the k+t] of prediction in the period, for optimization Control variable；δalj(k+n) it is the control variable of deferrable load, in should also meeting in a few days to optimize in addition to it need to meet formula (29) constraint The constraint of formula (13).
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CN111049166A (en) * 2020-01-03 2020-04-21 香港中文大学（深圳） Micro-grid optimization configuration method and device and computer readable storage medium
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CN113241757A (en) * 2021-04-21 2021-08-10 浙江工业大学 Multi-time scale optimization scheduling method considering flexible load and ESS-SOP

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CN108695903B (en) * 2018-06-19 2021-09-14 南京邮电大学 Micro-grid optimization scheduling method based on particle swarm optimization algorithm
CN109345019A (en) * 2018-10-10 2019-02-15 南京邮电大学 A kind of micro-capacitance sensor economic load dispatching optimisation strategy based on improvement particle swarm algorithm
CN109345019B (en) * 2018-10-10 2021-08-31 南京邮电大学 Improved particle swarm algorithm-based micro-grid economic dispatching optimization strategy
CN109088442A (en) * 2018-10-29 2018-12-25 国网山东省电力公司日照供电公司 Micro- energy net Optimal Operation Model of a variety of energy storage is considered under Multiple Time Scales
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CN109672216A (en) * 2018-11-26 2019-04-23 南瑞集团有限公司 One kind being based on polyfactorial active distribution network hierarchical control method and system
CN109617142A (en) * 2018-12-13 2019-04-12 燕山大学 A kind of CCHP type micro-capacitance sensor Multiple Time Scales Optimization Scheduling and system
CN109765787B (en) * 2019-01-30 2022-05-13 南方电网科学研究院有限责任公司 Power distribution network source load rapid tracking method based on intraday-real-time rolling control
CN109765787A (en) * 2019-01-30 2019-05-17 南方电网科学研究院有限责任公司 Power distribution network source load rapid tracking method based on intraday-real-time rolling control
CN110148969A (en) * 2019-03-26 2019-08-20 上海电力学院 Active distribution network optimizing operation method based on model predictive control technique
CN110137942A (en) * 2019-04-23 2019-08-16 河海大学 Multiple Time Scales flexible load rolling scheduling method and system based on Model Predictive Control
CN110137942B (en) * 2019-04-23 2022-09-16 河海大学 Multi-time scale flexible load rolling scheduling method and system based on model predictive control
CN110059897A (en) * 2019-05-23 2019-07-26 合肥工业大学 Active power distribution network based on MIXED INTEGER PSO algorithm in a few days rolling optimization method
CN110059897B (en) * 2019-05-23 2021-03-09 合肥工业大学 Active power distribution network intraday rolling optimization method based on mixed integer PSO algorithm
CN110311421A (en) * 2019-07-12 2019-10-08 燕山大学 Micro-capacitance sensor Multiple Time Scales energy management method based on Demand Side Response
CN110311421B (en) * 2019-07-12 2021-05-07 燕山大学 Micro-grid multi-time scale energy management method based on demand side response
CN110837916A (en) * 2019-09-30 2020-02-25 国创新能源汽车能源与信息创新中心(江苏)有限公司 Energy rolling optimization management method applied to home villa scene
CN110867902A (en) * 2019-10-15 2020-03-06 东北大学 Power generation prediction-based micro-grid distributed power supply de-centering optimized operation method
CN111049166A (en) * 2020-01-03 2020-04-21 香港中文大学（深圳） Micro-grid optimization configuration method and device and computer readable storage medium
CN111934360B (en) * 2020-07-09 2021-08-31 浙江浙能技术研究院有限公司 Virtual power plant-energy storage system energy collaborative optimization regulation and control method based on model predictive control
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CN113241757A (en) * 2021-04-21 2021-08-10 浙江工业大学 Multi-time scale optimization scheduling method considering flexible load and ESS-SOP
CN113241757B (en) * 2021-04-21 2022-06-17 浙江工业大学 Multi-time scale optimization scheduling method considering flexible load and ESS-SOP

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