CN108197766A - A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group - Google Patents

A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group Download PDF

Info

Publication number
CN108197766A
CN108197766A CN201810249572.1A CN201810249572A CN108197766A CN 108197766 A CN108197766 A CN 108197766A CN 201810249572 A CN201810249572 A CN 201810249572A CN 108197766 A CN108197766 A CN 108197766A
Authority
CN
China
Prior art keywords
model
distribution network
capacitance sensor
micro
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201810249572.1A
Other languages
Chinese (zh)
Inventor
李直
易灵芝
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xiangtan University
Original Assignee
Xiangtan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xiangtan University filed Critical Xiangtan University
Priority to CN201810249572.1A priority Critical patent/CN108197766A/en
Publication of CN108197766A publication Critical patent/CN108197766A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • 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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • 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
    • H02J3/381—Dispersed generators
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Power Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a kind of active distribution network Optimal Operation Models for including micro-capacitance sensor group, with the access of distributed generation resource and micro-capacitance sensor, controlled member increases in active distribution network, the Optimization Scheduling of conventional electrical distribution net has not been suitable for active distribution network Optimized Operation, therefore proposes that a kind of new method improves the economy of stability of power system and user side.It is characterized in that：It is proposed a kind of dual-layer optimization scheduling model of the active distribution network suitable for the group containing micro-capacitance sensor, upper layer model is using power distribution network as research object, optimization aim is improves power quality, reduces line loss, and for underlying model using micro-capacitance sensor as research object, optimization aim is minimum for cost.The present invention solves upper strata Optimized model using genetic algorithm, and lower floor's Optimized model is solved using mixed integer linear programming.

Description

A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group
Technical field
The present invention relates to power system optimal dispatch fields, specifically excellent with a kind of active distribution network comprising micro-capacitance sensor group Change scheduling model and solve power train load scheduling problem.
Background technology
Economic Dispatch is on the basis of power grid security reliability service is ensured, is made full use of existing in power grid Power transmission and distribution and transformer equipment by scientific and effective method, preferably transformer, circuit way of economic operation, load economic allotment Deng, reduce the loss of electric system, utmostly realize energy utilization maximize；
Nowadays, electricity market becomes high competition, more urgent to the increase of energy requirement.Economic load dispatching is modern One of effective means in the planning of energy management system running.To reducing production cost and increasing system reliability, economy load It dispatches and is played a crucial role in the economy for maintaining electric system.
The efficiency in fuel cost and power station determines the operating cost produced electricl energy, so as to economic load dispatching problem A very important task in Operation of Electric Systems and planning is become.Its main target be optimization available cell between Power generation, so as to make total power production cost minimum while constraint (all equatioies and the inequality constraints) that system is considered is met Change.
Invention content
For the difficulties and problems in the presence of solution Economic Dispatch problem, the invention discloses one kind containing micro- The active distribution network Optimal Operation Model of power grid group
The present invention is mainly realized by following scheme：
A kind of active distribution network Optimal Operation Model of the group containing micro-capacitance sensor；
Active distribution network system capacity Governance framework containing more microgrids is as shown in Figure 1.It is different from conventional electrical distribution net, actively match Containing distributed generation resource and flexible load in power grid, the element controlled is needed to become more, while dispatch to power distribution network with optimization means The requirement at center (DNO) also higher.Micro-capacitance sensor group access power distribution network after, due to micro-capacitance sensor it is relatively independent the characteristics of, actively Power distribution network can only carry out energy management by PCC to it.
It accesses in the typical micro-capacitance sensor of active distribution network comprising miniature gas turbine (MT), fuel cell (FC), accumulator (battery), wind turbine (WT) and photovoltaic (PV) distributed power supply and node load.Micro-capacitance sensor both to active distribution network sell electricity or Electricity can be bought to it.
In the bilevel optimization scheduling model that this patent proposes, upper layer model need to control the load for being directly accessed power distribution network, The power that exchanges of distributed generation resource and PCC, PCC power that underlying model needs are provided according to upper layer model are controlled in micro-capacitance sensor Load and distributed generation resource, scheduling result is then fed back into upper layer model, by the iteration of upper layer model and underlying model, Scheduling result after being restrained.Detailed model construction is in the specific introduction of lower section.
Fig. 1 is the active distribution network block schematic illustration of the group containing micro-capacitance sensor；
Fig. 2 is model solution flow chart；
Specific embodiment：
Below in conjunction with the accompanying drawings, the present invention is described in further detail.
Active distribution network system capacity Governance framework containing more microgrids is as shown in Figure 1.It is different from conventional electrical distribution net, actively match Containing distributed generation resource and flexible load in power grid, the element controlled is needed to become more, while dispatch to power distribution network with optimization means The requirement at center (DNO) also higher.Micro-capacitance sensor group access power distribution network after, due to micro-capacitance sensor it is relatively independent the characteristics of, actively Power distribution network can only carry out energy management by PCC to it.
It accesses in the typical micro-capacitance sensor of active distribution network comprising miniature gas turbine (MT), fuel cell (FC), accumulator (battery), wind turbine (WT) and photovoltaic (PV) distributed power supply and node load.Micro-capacitance sensor both to active distribution network sell electricity or Electricity can be bought to it.
In the bilevel optimization scheduling model, upper layer model needs to control load, the distributed generation resource for being directly accessed power distribution network With the power that exchanges of PCC, underlying model needs the PCC power provided according to upper layer model to control the load in micro-capacitance sensor with dividing Then scheduling result is fed back to upper layer model by cloth power supply, by the iteration of upper layer model and underlying model, after obtaining convergence Scheduling result.Detailed model construction is in the specific introduction of lower section.
Fig. 2 is the flow chart that model solution flow chart solves electric system economic load dispatching.First to required in algorithm The model and formula wanted are illustrated, i.e. the object function and constraints of the active distribution network scheduling model of the group containing microgrid.
1. upper strata Optimized model
The research object of upper strata Optimized model is active distribution network, to improve power supply quality with reducing current supply loss as mesh Mark, the exchange power of PCC is decision variable.
1.1 object function
The object function of upper layer model includes line loss and power distribution network node voltage degrees of offset[17], specifically describe It is as follows：
minCupper=μlossCloss+μVoffCVoff (1)
Wherein, ClossTotal network loss function for power distribution network in dispatching cycle；CVoffLetter is deviated for each node voltage of power distribution network Number, to weigh the major criterion of distribution network electric energy quality；μlossWith μVoffRespectively ClossWith CVoffWeight in object function Coefficient.
In object function, the concrete form of network loss function and variation function see respectively formula with.
Wherein, T is dispatching cycle, is set as herein 24 hours；L is distribution network line total number；N is always a for power distribution network node Number；Δ t is the time scale of Optimized Operation, is set as herein 1 hour；Ploss,l(t) it is line losses of the circuit l in the t periods, by Load flow calculation acquires；Un(t) it is perunit values of the node n in the node voltage of t periods；Δ U is the threshold of node voltage offset penalties Value, is set as 0.05 herein；[x]+It represents the downward rounding operations of x.
When formula represents that node voltage offset is smaller, i.e. Un(t) ∈ [1- Δs U, 1+ Δ U], then penalty term [| Un(t)-1|/Δ U]+It is 0, variation function is smaller；Otherwise, penalty term is more than 1, and the value of variation function significantly increases.
1.2 constraints
1) trend constraint：
In formula, subscript DNG, MG and L represent active distribution network, micro-capacitance sensor and load respectively；Subscript n represents power distribution network with m Node serial number；P and Q represents active power and reactive power respectively；V represents node voltage；GnmWith BnmNode n and m are represented respectively Between conductance and susceptance；θnm(t) phase angle difference between t period node n and m.
2) distributed generation resource constrains：
Assuming that the power supply of access active distribution network is controllable unit, then the power constraints for being directly accessed active distribution network include The constraint of output power bound, minimum start-off time constraints and Climing constant, respectively such as formula, with it is shown.
-Δdown,i≤Pi(t)-Pi(t-1)≤Δup,i (7)
Wherein, Pi(t) and Qi(t) active power output of t periods controllable unit i and idle output are represented respectively；P i、Table respectively Show the active power output lower limit of controllable unit i and the output upper limit；Q i、The idle output lower limit and output of controllable unit i is represented respectively The upper limit；Ui(t) operating status of controllable unit i is represented, 0 represents that stoppage in transit, 1 represent operation；Ustart,i(t)、Ushut,i(t) it is respectively The booting decision variable of controllable unit i and shutdown decision variable；MOTiThe minimum available machine time for controllable unit i；MDTiIt is controllable The minimum available machine time of unit i；Δdown,i、Δup,iThe maximum downwardly and upwardly climbing rate of controllable unit i is represented respectively.
3) PCC power constraints
In formula, PPCC(t) the exchange power of t period PCC, i.e. Power Exchange between active distribution network and micro-capacitance sensor are represented；Represent that PCC exchanges the upper limit of power.
2. lower floor's Optimized model
The research object of lower floor's Optimized model is micro-capacitance sensor, and using the cost that runs minimized as target, dispatching can in micro-capacitance sensor Power supply is controlled to contribute.
2.1 object function
Lower floor's Optimized model object function is the cost that runs minimized, and is described in detail below：
Wherein, Cfuel,i() is controllable electric power fuel cost function, including miniature gas turbine and fuel cell；Ui(t) Represent the operating status of t periods controllable electric power operation, 0 represents not run, and 1 represents operation；CS,i() for controllable electric power start into This；COM,i(·)、CDP,i(·)、CE,i() represents maintenance cost, depreciable cost and the Environmental costs of controllable unit respectively；COM,k (·)、CDP,k() represents maintenance cost, the depreciable cost of energy storage device respectively；Cbuy(·)、Csell() represents microgrid respectively From the cost of active distribution network power purchase and the income of sale of electricity；PM,i(t) controllable unit output in micro-capacitance sensor is represented；PM,i(t) it represents Accumulator cell charging and discharging power in micro-capacitance sensor；Pbuy(t)、Psell(t) represent micro-capacitance sensor from active distribution network power purchase and sale of electricity respectively Power.
The function of controllable unit fuel cost, start-up cost, maintenance cost, depreciable cost and Environmental costs in formula is shown in formula ~：
Cfuel,i(PM,i(t))=ai·PM,i(t)2+bi·PM,i(t)+ci (10)
CS,i(PM,i(t))=SiUM,start,i(t) (11)
COM,i(PM,i(t))=KOM,iPM,i(t)Δt (12)
Wherein, ai、biAnd ciCoefficient for i-th of controllable unit fuel cost conic section；SiFor i-th of controllable unit Start-up cost；UM,start,i(t) the booting decision variable for i-th of controllable unit in micro-capacitance sensor；Caz,iFor i-th of controllable electric The present worth of the installation cost as per machine capacity in source；kiCapacity factor for i-th of micro- source；niService life for i-th of micro- source； KOM,iUnit quantity of electricity operation expense coefficient for i-th of micro- source；AjSpecific emissions expense for jth item pollutant；BijFor The jth item pollutant discharge amount of i-th of micro- source unit quantity of electricity；J is the type of pollutant；
Maintenance cost, the depreciable cost function of energy storage device in formula see formula,：
COM,k(Pk(t))=KOM,k|Pk(t)|Δt (15)
CDP,k(Pk(t))=(Erated,kCE,k+Prated,kCP,k)Lloss,k (16)
Wherein, KOM,kUnit quantity of electricity operation expense coefficient for k-th of accumulator group；Erated,k、Prated,kRespectively The rated capacity and rated power of k-th of accumulator group, CE,k、CP,kThe respectively unit capacity and unit power of accumulator group k The present worth of installation cost；Lloss,kLife consumption coefficient for accumulator group k[18]。
In formula micro-capacitance sensor power purchase expense and sale of electricity revenue function such as formula with it is shown.
Cbuy(Pbuy(t))=pbuy(t)Pbuy(t) (17)
Csell(Psell(t))=psell(t)Psell(t) (18)
Wherein, pbuy(t) it is t periods micro-capacitance sensor to active distribution network purchase electricity price；pbuy(t) it is t periods micro-capacitance sensor to master Dynamic power distribution network sale of electricity electricity price.
2.2 constraints
Micro-capacitance sensor ignores its trend constraint because system scale is smaller.
1) power-balance constraint：
2) Reserve Constraint：
Wherein, PM,i(t)、Pk(t)、Pw(t)、Pp(t)、Pbuy(t)、Psell(t) represent that controllable unit goes out in micro-capacitance sensor respectively Power, accumulator output, wind power output, solar power generation output and the power purchase to active distribution network and sale of electricity；Pd(t) it is micro-capacitance sensor Workload demand；R (t) is the spinning reserve demand of micro-capacitance sensor.
2) distributed generation resource constrains：
Distributed generation resource in micro-capacitance sensor includes wind turbine, photovoltaic and controllable unit, wherein the constraint of controllable unit with directly Access active distribution network controllable Unit commitment it is similar, see formula~, details are not described herein.Wind-powered electricity generation, photovoltaic output should not exceed It, which is predicted, contributes, constraint such as formula, shown.
0≤Pw(t)≤Pw,fore(t) (21)
0≤Pp(t)≤Pp,fore(t) (22)
Wherein, Pw,fore(t) it contributes for wind turbine prediction；Pp,fore(t) it contributes for photovoltaic generation prediction.
3) accumulator constrains
The charge power and discharge power limit value of accumulator such as formula, shown：
Wherein, Pch,k(t)、Pdh,k(t) it is respectively charge power, discharge power, the output of accumulator meets Pk(t)= Pdh,k(t)-Pch,k(t)；P ch,k(t)、The respectively lower limit and the upper limit of charge power；P dh,k(t)、Respectively put The lower limit and the upper limit of electrical power.Uch,k(t)、Udh,k(t) it is 0,1 variable, represents the discharge condition of accumulator and charging shape respectively State, the charging and discharging state of accumulator is mutual exclusion, i.e. Udh,k(t)+Uch,k(t)≤1。
The residual capacity of accumulator is more than that limit value can cause accumulator very big damage, and constraint is as shown in formula：
Wherein,E k、The minimum and maximum value of remaining battery capacity is represented respectively.
Accumulator moment t residual capacity with the residual capacity of its previous moment t- Δs t and its from t- Δs t to t Discharge and recharge it is related, during charge and discharge, the dump energy calculation formula of accumulator is：
Wherein, Ek(t)、Ek(t-1) it is respectively moment t, the remaining battery capacity (kWh) of t-1；ηch,k、ηdh,kIt is respectively The charge efficiency and discharging efficiency of accumulator；σkIt is the self-discharge rate of accumulator.
To ensure the normal consistency in a micro-capacitance sensor lower period, accumulator can generally keep residual capacity within dispatching cycle First and last balances, as shown in formula.
Ek(0)=Ek(T) (27)
4) PCC power constraints
The power purchase power of micro-capacitance sensor allows to exchange within the upper limit of the power with sale of electricity power constraint in PCC, see formula with.
Since active distribution network participates in scheduling PCC power, power purchase power and the sale of electricity power of micro-capacitance sensor should meet formula.
Psell(t)-Pbuy(t)=PPCC(t) (30)。

Claims (2)

1. a kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group, it is characterised in that：To include the master of micro-capacitance sensor group Dynamic distribution network system is research object, establishes a kind of bilevel optimization tune of the completely new active distribution network suitable for the group containing micro-capacitance sensor Model is spent, for the upper layer model of bi-level optimization model using active distribution network as object, optimization aim is respectively to reduce network loss, improve confession Electricity quality；For underlying model using micro-capacitance sensor as research object, object function is minimum for cost, and upper layer model passes through public affairs with underlying model Tie point (PCC) carries out Power Exchange altogether, and the genetic algorithm of application enhancements is solved with mixed integer linear programming algorithm respectively Upper strata Optimized model and lower floor's Optimized model finally by taking the IEEE33 node power distribution nets after adjustment as an example, are demonstrated and are built herein The validity of model.
2. thinking and the realization of a kind of active distribution network Optimal Operation Model comprising micro-capacitance sensor group based on claim 1 proposition Process, the solution of entire model are divided into levels, its specific implementation step is as follows：
Upper strata model solution algorithm
Step 1：Initialization model parameter, including genetic algorithm parameter, active distribution network parameter, load prediction etc.；
Step 2：Population is initialized, it is random to generate individual in population, to the decision variable in individual using real coding, wherein determining Plan variable includes trend constraint, distributed generation resource is contributed, PCC exchanges power etc.；
Step 3：Fitness value is calculated, fitness function is the object function of upper strata scheduling model；
Step 4：Selected population, according to the individual of roulette principle selection optimization；
Step 5：Intersect and make a variation, population is intersected and made a variation according to adaptive principle；
Step 6：Terminate decision criteria, if optimal solution meets the condition of convergence or reaches highest iterations, export calculating As a result；Otherwise, return to step 3；
Underlying model derivation algorithm
The constraint of lower floor's scheduling model can largely be changed to linear restriction, and wherein nonlinear restriction only has controllable unit cost function It is lost and constrains with accumulator, wherein controllable unit cost function is quadratic function, piece-wise linearization is used to handle it, and electric power storage The linearisation of pond loss constraint carries out piece-wise linearization processing to it, and after linearization process, lower floor's scheduling problem just converts For Mixed integer linear programming, and solve Mixed integer linear programming and model is solved using CPLEX.
CN201810249572.1A 2018-03-23 2018-03-23 A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group Pending CN108197766A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810249572.1A CN108197766A (en) 2018-03-23 2018-03-23 A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810249572.1A CN108197766A (en) 2018-03-23 2018-03-23 A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group

Publications (1)

Publication Number Publication Date
CN108197766A true CN108197766A (en) 2018-06-22

Family

ID=62595903

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810249572.1A Pending CN108197766A (en) 2018-03-23 2018-03-23 A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group

Country Status (1)

Country Link
CN (1) CN108197766A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108985502A (en) * 2018-07-02 2018-12-11 国网江西省电力有限公司经济技术研究院 A kind of country-provincial two layers of electric energy superseding programming model and algorithm frame
CN109472493A (en) * 2018-11-09 2019-03-15 浙江大学 A kind of energy conversion device service life Explore of Unified Management Ideas based on genetic algorithm
CN109713732A (en) * 2019-01-17 2019-05-03 南京邮电大学 A kind of method of more micro-capacitance sensors and power distribution network coordination optimization scheduling
CN110601203A (en) * 2019-09-30 2019-12-20 重庆大学 Piecewise linearization optimal power flow calculation method for electric-gas coupling system
CN113270882A (en) * 2021-04-26 2021-08-17 南方电网电动汽车服务有限公司 Method, device, equipment and medium for reducing network loss of power distribution network through energy storage device
CN113471976A (en) * 2021-07-14 2021-10-01 国网江苏省电力有限公司营销服务中心 Optimal scheduling method based on multi-energy complementary micro-grid and active power distribution network

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103761586A (en) * 2014-02-11 2014-04-30 东南大学 Microgrid cluster concentrated-distributed type coordinated optimization scheduling method
CN105976108A (en) * 2016-05-05 2016-09-28 国网浙江省电力公司电力科学研究院 Distributed energy storage planning method of power distribution network
CN106712114A (en) * 2017-02-24 2017-05-24 国网江苏省电力公司泗阳县供电公司 Layered energy optimization scheduling method of active power distribution network considering environmental benefits
US20170194814A1 (en) * 2014-04-23 2017-07-06 Nec Corporation Electricity distribution system with dynamic cooperative microgrids for real-time operation
CN107392395A (en) * 2017-08-23 2017-11-24 天津大学 A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103761586A (en) * 2014-02-11 2014-04-30 东南大学 Microgrid cluster concentrated-distributed type coordinated optimization scheduling method
US20170194814A1 (en) * 2014-04-23 2017-07-06 Nec Corporation Electricity distribution system with dynamic cooperative microgrids for real-time operation
CN105976108A (en) * 2016-05-05 2016-09-28 国网浙江省电力公司电力科学研究院 Distributed energy storage planning method of power distribution network
CN106712114A (en) * 2017-02-24 2017-05-24 国网江苏省电力公司泗阳县供电公司 Layered energy optimization scheduling method of active power distribution network considering environmental benefits
CN107392395A (en) * 2017-08-23 2017-11-24 天津大学 A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108985502A (en) * 2018-07-02 2018-12-11 国网江西省电力有限公司经济技术研究院 A kind of country-provincial two layers of electric energy superseding programming model and algorithm frame
CN108985502B (en) * 2018-07-02 2022-02-15 国网江西省电力有限公司经济技术研究院 Planning method and algorithm framework based on two-layer electric energy substitution planning model
CN109472493A (en) * 2018-11-09 2019-03-15 浙江大学 A kind of energy conversion device service life Explore of Unified Management Ideas based on genetic algorithm
CN109472493B (en) * 2018-11-09 2021-11-30 浙江大学 Unified management method for service life of energy conversion device based on genetic algorithm
CN109713732A (en) * 2019-01-17 2019-05-03 南京邮电大学 A kind of method of more micro-capacitance sensors and power distribution network coordination optimization scheduling
CN109713732B (en) * 2019-01-17 2021-03-30 南京邮电大学 Method for coordinated optimization scheduling of multiple micro-grids and power distribution network
CN110601203A (en) * 2019-09-30 2019-12-20 重庆大学 Piecewise linearization optimal power flow calculation method for electric-gas coupling system
CN113270882A (en) * 2021-04-26 2021-08-17 南方电网电动汽车服务有限公司 Method, device, equipment and medium for reducing network loss of power distribution network through energy storage device
CN113471976A (en) * 2021-07-14 2021-10-01 国网江苏省电力有限公司营销服务中心 Optimal scheduling method based on multi-energy complementary micro-grid and active power distribution network
CN113471976B (en) * 2021-07-14 2023-11-07 国网江苏省电力有限公司营销服务中心 Optimal scheduling method based on multi-energy complementary micro-grid and active power distribution network

Similar Documents

Publication Publication Date Title
Wang et al. Interactive dispatch modes and bidding strategy of multiple virtual power plants based on demand response and game theory
CN108197766A (en) A kind of active distribution network Optimal Operation Model for including micro-capacitance sensor group
Chen et al. Optimal allocation and economic analysis of energy storage system in microgrids
CN106160091B (en) Promote the electric automobile charging station charge and discharge dispatching method of regenerative resource consumption
CN107633333A (en) The source lotus storage method for optimizing scheduling and system of flexible transformer station's regional power grid
CN105375507A (en) Power two-stage interactive optimization scheduling system of virtual power plant in haze environment
Allwyn et al. A comprehensive review on energy management strategy of microgrids
Tomin et al. Deep reinforcement learning for energy microgrids management considering flexible energy sources
CN105896596B (en) A kind of the wind power layering smoothing system and its method of consideration Demand Side Response
CN108462198A (en) A kind of microgrid Optimization Scheduling of providing multiple forms of energy to complement each other based on multi-agent technology
CN108233430A (en) A kind of alternating current-direct current mixing microgrid optimization method of meter and system energy fluctuation
CN109412158A (en) A kind of sending end power grid Unit Combination progress control method for considering to abandon energy cost constraint
Zhu et al. Energy storage in high variable renewable energy penetration power systems: technologies and applications
CN112383086B (en) Island micro-grid day-ahead energy-standby combined optimization scheduling method
CN116231765B (en) Virtual power plant output control method
Xing et al. Multi-objective optimization and dispatch of distributed energy resources for renewable power utilization considering time-of-use tariff
CN112311017A (en) Optimal collaborative scheduling method for virtual power plant and main network
CN117436773B (en) Independent micro-grid resource planning method and system containing interruptible load
Mao et al. Microgrid group control method based on deep learning under cloud edge collaboration
Shafiee et al. A novel stochastic framework based on PEM-DPSO for optimal operation of microgrids with demand response
CN110247392A (en) More standby resources robust Optimal methods of meter and wind-powered electricity generation marginal capacity and Demand Side Response
Ren et al. Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode
CN117559526A (en) Router-simulated energy regulation and control method based on optical storage and charging integrated charging station
CN116914732A (en) Deep reinforcement learning-based low-carbon scheduling method and system for cogeneration system
CN116865270A (en) Optimal scheduling method and system for flexible interconnection power distribution network containing embedded direct current

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20180622

WD01 Invention patent application deemed withdrawn after publication