CN108009693A - Grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response - Google Patents
Grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response Download PDFInfo
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- 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
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- 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
- 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
-
- 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]
Abstract
The present invention relates to a kind of grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response, demand response layer optimizes guiding user power utilization Proactive traceback clean energy resource generated output by level-one, the Efficient Development of clean energy resource is promoted to utilize, proportion of the clean energy resource in final energy consumption is improved, more stable electric load operating status is formed by adjustment of the 2-level optimization to demand load and improves the output stability in controllable micro- source.Microgrid layer then minimizes integrated operation cost from micro-capacitance sensor economy point on this basis.Take into full account based on the Demand Side Response of tou power price and model is solved to build rational grid-connected micro-capacitance sensor bi-level optimal model and adopt the genetic algorithm based on simulated annealing proposed, improve the convergence rate and precision of model solution.
Description
Technical field
The present invention relates to a kind of micro-capacitance sensor economic optimization dispatching technique, it is more particularly to a kind of based on two-stage demand response and
Net micro-capacitance sensor dual blank-holder.
Background technology
It is increasingly prominent with environment and energy problem, distributed energy with its it is environmental-friendly and renewable the features such as become
The hot spot studied in the industry.But power quality and stable will be brought to power grid using its fluctuation during direct grid-connected and intermittence
Deng great number of issues (such as document 1：In building up, slow Fujian, Xu Ke, waits impact analysis [J] electricity of the distributed generation resources access to power grid
Force system and its automation journal, 2012,24 (1)：138-141；Document 2：Ahn S J, Nam S R, Choi J H, et
al.Power Scheduling of Distributed Generators for Economic and Stable
Operation of a Microgrid [J] .IEEE Transactions on Smart Grid, 2013,4 (1)：398-
405).Micro-capacitance sensor as distributed generation resource access power distribution network effective organizational form, become solve the problems, such as this key technology it
One (such as document 3：Hatziargyriou N, Asano H, Iravani R, et al.Microgrids [J] .IEEE Power
And Energy Magazine, 2007,5 (4)：78-94；Document 4：Katiraei F, Iravani R, Hatziargyriou
N, et al.Microgridsmanagement [J] .IEEE Power and Energy Magazine, 2008,6 (3)：54-
65)。
A series of small electrical system that micro-capacitance sensor is formed as distributed generation resources, its economical operation and Optimized Operation are micro-
Important topic in net research.The target of microgrid Optimized Operation be generally divided into construction cost, operation expense, environmental benefit into
Originally, reliability and network loss etc..(Zhu Lan, solemn and just, Yang Xiu, wait wind-light storage micro-grid system accumulator capacities to distribute rationally to document 5
Technique study [J] electric power network techniques, 2012,36 (12)：Wind-light storage complementary power generation system model 26-31) is established, using at night
Between wind energy is sufficient, the load low period charges storage battery, solar energy sufficient, load high period on daytime using solar energy with
Wind energy, energy storage device generate electricity to meet to power jointly.(Wang Chengshan, big vast blog article, Guo Li, wait supply of cooling, heating and electrical powers microgrids excellent to document 6
Change scheduling universal modeling method [J] Proceedings of the CSEEs, 2013,33 (31)：26-33) propose based on supply of cooling, heating and electrical powers
Bus-type structure and devise corresponding dynamic economic dispatch Optimized model, using 0-1 mixed integer linear programming to model
Solved, demonstrate the feasibility and validity of structure.Document 7-9 (documents 7：Chen Jie, Yang Xiu, Zhu Lan, wait the more mesh of microgrids
Mark economic load dispatching optimization [J] Proceedings of the CSEEs, 2013 (19)：57-66；Document 8：Peng Chunhua, Xie Peng, Zhan Jiwen,
Deng based on microgrid robust economic load dispatching [J] the electric power network techniques for improving bacterial foraging algorithm, 2014,38 (9)：2392-2398；Text
Offer 9：Wu Xiong, Wang Xiuli, Wang Jianxue, waits Integer programming [J] the China electrical engineering of microgrid Economic Dispatch Problems
Journal, 2013,33 (28)：1-8) using a micro-capacitance sensor comprising regenerative resource, electric energy storage, co-generation unit etc. as research
Object, analyzes the optimal output that the assembly such as depreciation, maintenance and cogeneration of heat and power fuel cost in meter and net runs this lower each unit.
Since optimization problem is a typical NP-hard problem, it can meet multiple indexs at the same time in optimization process
Constraints, be consequently adapted to solve it in (such as document 10 using intelligent algorithm：Guo Li, Liu Wenjian, Jiao Bingqi,
Deng multi-objection optimization planning design method [J] Proceedings of the CSEEs of independent microgrid systems, 2014,34 (4)：524-
536), excellent effect.(Liu Xiaoping, Ding Ming, Zhang Yingyuan, wait dynamic economic dispatch [J] the China motor of micro-grid systems to document 11
Engineering journal, 2011,31 (31)：The micro-grid system dynamic economic dispatch model based on chance constrained programming 77-84) is proposed,
And model is solved with the genetic algorithm with reference to Monte Carlo simulation.(Xiong Yan, Wu Jiekang, Wang Qiang, wait honourable to document 12
The supply of cooling, heating and electrical powers optimization Coordination Model and method for solving [J] Proceedings of the CSEEs of gas storage complemental power-generation, 2015,35
(14)：Different rate structures 3616-3625) are combined, establish wind-light storage and the cool and thermal power cool and thermal power of natural gas complementary power generation
Alliance optimizes Coordination Model, and model is solved using NR-PSO algorithms, it is shown that the effect of multi-source complementation and advantage.Text
Offer 13 (Morais H, Kadar P, Faria P, et al.Optimal scheduling ofa renewable micro-
grid in an isolated load area usingmixed-integer linear programming[J]
.Renewable Energy, 2010,35 (1)：151-156) propose the mixed integer linear programming mould of microgrid energy optimization
Type, the model is solved using CPLEX branch-bound algorithms, and good precision is obtained while ensureing and calculating the time.Document
14 (Yang Peipei, Ai Xin, Cui Mingyong, wait the microgrid economic operation analysis of containing plurality of energy supplies systems of the based on particle swarm optimization algorithm
[J] electric power network techniques, 2009,33 (20)：38-42) according to the steady-state characteristic of distributed generation resource in terms of cost and benefit 2 with
The minimum object function of microgrid operating cost, establish consider greenhouse gases, pollutant emission microgrid economic model and use grain
Sub- colony optimization algorithm solves model.(Zhao Bo, with assurance and composure, Xu Zhicheng, waits to consider the light storage of Demand Side Response to bag to document 15
Grid type micro-capacitance sensor distributes [J] Proceedings of the CSEEs, 2015,35 (21) rationally：5465-5474) utilize combinatorial programming knot
Close particle cluster algorithm to solve the Demand Side Response model established, analyze Demand Side Response and micro- electricity is stored up to grid type light
Net economic benefit and the influence of energy storage configuration.Document 16-17 (documents 16：Yuan Tiejiang, Chao Qin, spit Er Xunyi and do not draw sound, wait
Modeling [J] Proceedings of the CSEEs of large-scale wind power interconnected electric power system kinetic cleaning economic optimization scheduling, 2010,30
(31)：7-13；Document 17：Chen Gonggui, Chen Jin richness power system environments containing wind power plant Economic Intelligence scheduling modeling and algorithm [J]
Proceedings of the CSEE, 2013,33 (10)：27-35) dynamically optimized scheduling of wind power plant electric system is modeled, and
Genetic algorithm is respectively adopted and improves the scheduling model that quantum telepotation Algorithm for Solving is established.
Demand Side Response (demand response, DR) is that Utilities Electric Co. passes through discount electricity price or high price compensation equal excitation
Mode come guide user change consumption mode (such as document 18：Zhu Lan, solemn and just, Yang Xiu, wait the microgrid of meters and Demand Side Response comprehensive
Joint source planing method [J] Proceedings of the CSEEs, 2014,34 (16):2621-2628), realize supply and demand interaction, improve negative
Lotus rate, reaches the process of power balance.Demand Side Response generally comprises direct load control (document 19：Kurucz C N,
Brandt D, Sim S.A linear programming model for reducing system peak through
Customer load control programs [J] .IEEE Transactions on Power Systems, 1996,11
(4)：1817-1824；Document 20：Ruiz N,Cobelo I,Oyarzabal J.A Direct Load Control Model
for Virtual Power Plant Management[J].IEEE Transactions on Power Systems,
2009,24(2):959-966；Document 21：Yao L,Lu H R.A Two-Way Direct Control of Central
Air-Conditioning Load Via the Internet[J].IEEE Transactions on Power
Delivery,2009,24(1):240-248；Document 22：Ziaii M,Kazemi A,Fotuhi-Firuzabad M,et
al.A Method to Calculate the Linear Load Pay Back Factors for Air
Conditioners[C]//Power and Energy Engineering Conference.2012:1-5), Demand-side is competing
Valency, interruptible load and urgent need response etc..(Zhai Qiaozhu, Wang Lingyun Demand Side Responses are to reducing cost of electricity-generating for document 23
Benefit estimates [J] Proceedings of the CSEEs, 2014,34 (7):1198-1205) one kind is proposed for Demand Side Response to estimate
Meter method, for inducing more rational system total load curve, reduces the operating cost in power generation.24 (grandson of document
Space army, Li Yang, Wang Beibei, wait to count and the model of operation plan a few days ago [J] electric power network techniques of uncertain demand response, and 2014,
38(10):2708-2714) construct the scheduling a few days ago of the uncertain Response Mechanism comprising tou power price and interruptible load mechanism
Planning model, employs Integer programming and demonstrates influence of the user response uncertainty to operation plan a few days ago.
The content of the invention
The problem of the present invention be directed to economical operation and Optimized Operation being the important topic in microgrid research, it is proposed that a kind of
Grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response, takes into full account based on the Demand Side Response of tou power price come structure
Build rational grid-connected micro-capacitance sensor bi-level optimal model and adopt the genetic algorithm based on simulated annealing proposed and model is asked
Solution, minimizes the operating cost of microgrid, perform it is main consider direct load control, when scheduled, directly by corresponding load
Excision.
The technical scheme is that：A kind of grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response, specifically
Include the following steps：
1), according to each micro- source model of micro-grid system, all parameters of input model and constraints, annealing temperature T, most is selected
Big iterations GmaxAnd Population Size M；
2) level-one optimization population, is initialized：Clean energy resource is selected to optimize the energy, collection level-one optimization energy number as level-one
According to data message individual as an optimization, that each individual of level-one optimization includes：The electrical power P exported with the wind energy conversion system of periodwt,r、
Output power of photovoltaic module Ppv,r, level-one optimization in load the amount of being transferred to PLin,1With the amount of producing PLout,1；
3), each individual that step 2) produces is brought into object functionMeter
The target function value corresponding to it is calculated, since the individual of target function value minimum is optimal, and the fitness value of individual is up to
It is optimal, it is in order to which solving result is consistent, target function value is inverted as each individual fitness value；
4), selected to being ranked up from big to small in step 2) population individual fitness value, and retained using league matches
Then other individuals are carried out cross and variation again and operate to obtain corresponding new individual by optimum individual, and optimal with reservation
Individual forms new population；
5) and then before judgement whether iterations G is more than maximum iteration Gmax, as G is less than Gmax, then G=G+1, T=
T* τ, τ are annealing temperature decrement factor, then the new population that step 4) is formed enters step the iterative cycles 3) carried out next time,
Until G is more than or equal to Gmax, output optimum individual Pwt,r、Ppv,rAnd PL,12-level optimization and final microgrid are substituted into as definite value
Layer optimization, is transferred to 2-level optimization；
6) 2-level optimization population, is initialized：Iterations G and annealing temperature T is resetted, selection output is stablized, adjustable energy saving
As 2-level optimization's energy, collection 2-level optimization multi-energy data is individual as an optimization in source, without considering under load condition, according to each two
The history data of the level optimization energy obtains optimal output curve using similar fitting process, and it is excellent according to two level to set traffic department
Change energy operation conditions and equipment day repair schedule to its further revised gross capability it is expected supply curve, two level is excellent
Changing each individual includes first best individual Pwt,r、Ppv,rAnd PL,1, and with each energy output of period 2-level optimization, expectation supply
Curve；
7), each individual that step 6) produces is brought into object functionCalculate corresponding to it
Target function value, whereinFor the micro-grid system equivalent load after 2-level optimization,For micro-grid system it is expected supply, be due to
The individual of target function value minimum to be optimal, and the fitness value of individual be up to it is optimal, in order to which solving result is consistent, by target
Functional value is inverted as each individual fitness value；
8), select to be ranked up in step 6) population individual fitness value from big to small using league matches, and retain most
Excellent individual, then again carries out other individuals cross and variation and operates to obtain corresponding new individual, and optimal a with reservation
Body forms new population；
9) and then before judgement whether iterations G is more than maximum iteration Gmax, as G is less than Gmax, then G=G+1, T=
T* τ, τ are annealing temperature decrement factor, then the new population that step 8) is formed enters step the iterative cycles 7) carried out next time,
Until G is more than or equal to Gmax, export each energy output, the total transfer amount P of load in optimum individual micro-grid systemcl, and thus calculate
Go out the Spot Price rate P of corresponding optimal solution*；
10) finally determine that each controllable 2-level optimization Wei Yuan goes out by micro-grid system economic goal function and corresponding constraints
The economic load dispatching distribution of power.
The beneficial effects of the present invention are：Grid-connected micro-capacitance sensor dual blank-holder of the invention based on two-stage demand response,
Demand response layer optimizes guiding user power utilization Proactive traceback clean energy resource generated output by level-one, promotes the efficient of clean energy resource
Utilization, improve proportion of the clean energy resource in final energy consumption, are formed by adjustment of the 2-level optimization to demand load
More stable electric load operating status simultaneously improves the output stability in controllable micro- source.Microgrid layer is then on this basis from micro- electricity
Net economy point, which sets out, minimizes integrated operation cost.In addition, propose to carry out model based on the genetic algorithm of simulated annealing
Solve, improve the convergence rate and precision of model solution.
Brief description of the drawings
Fig. 1 is model solution flow chart of the present invention；
Fig. 2 is wind-powered electricity generation of the present invention, the prediction data of photovoltaic and optimization datagram；
Fig. 3 is present invention load optimal result figures at different levels；
Fig. 4 is each distributed energy generating optimization result figure；
Fig. 5 is inventive algorithm solving result comparison diagram；
Fig. 6 always optimizes capability diagram for clean energy resource of the present invention；
Fig. 7 always optimizes capability diagram for miniature combustion engine of the present invention；
Fig. 8 is load optimal curve map of the present invention.
Embodiment
It is several from microgrid layer Optimized model, demand response layer Optimized model, model solution algorithm, validation verification etc. below
The present invention will be further described for aspect.
1st, microgrid layer Optimized model
To realize comprehensive cost minimization, herein for grid-connected micro- electricity containing miniature combustion engine, wind-light storage and Demand-side load
Net is constructed comprising system investments depreciable cost, operation expense, environmental benefit cost, the integration objective letter for purchasing sale of electricity cost
Number.
1.1 object function
Calibration system synthesis cost object function be
In formula, F is the comprehensive cost of system day operation；To invest depreciable cost；For operation and maintenance cost；
For environmental benefit cost；The purchase sale of electricity cost interacted for system with grid power.
1) depreciable cost
Depreciable cost is typically the year value method such as use to convert cost of investment to get for unit Capacity Cost, belong to power plant and send out
Frozen composition (such as document 25 of electric cost：Wu Dongwen, combines to send outside kernel is based in coordinated scheduling in the extensive multi-sources of Ai Qing
Theoretical profit distribution [J] electric power network techniques, 2016,40 (10):2975-2981.), cost is dispatched with power plant and distribution of interests ceases
Manner of breathing closes, thus takes into account.The t periods depreciable cost in micro- source is (such as document 26：Massaeli M,Javadian
S A M,Khalesi N.Environmental benefits of DGs and comparing their generation
costs with thermal power plants considering production pollution on human
health[J].2011,15(2):1-6.)
In formula, nsFor the number in micro- source；riFor the fixation Annual Percentage Rate in i-th kind of micro- source；niFor pay back period of investment, value is to set
Standby average life span；cin,iFor the construction cost of unit capacity；kiFor annual capacity factor；For micro- source t moment output work
Rate.R in formulai、niAnd kiChoosing method bibliography [26].
2) operation and maintenance cost
O＆M cost includes fuel cost and maintenance cost, its model is
In formula, co&m,iFor i-th kind of micro- source specific power O＆M cost coefficient.
3) environmental benefit cost
Since miniature combustion engine equal distribution declines source using non-renewable energy resources such as natural gases as fuel, certain energy consumption is caused
And environmental pollution, therefore consider its environmental benefit cost, mainly include taking of both environmental value loss and the suffered fine of blowdown
With (such as bibliography 27：Ma Xiyuan, Wu Yaowen, Fang Hualiang, wait micro- using the wind/light/storage mixing for improving bacterial foraging algorithm
Electric network source distributes [J] Proceedings of the CSEEs, 2011,31 (25) rationally:17-25.), its model is：
In formula, npFor the species of pollutant；QijFor i-th kind of distributed generation resource output unit electricity when jth pollutant
Discharge capacity, g/ (kWh)；For the environmental value of jth pollutant；VjFor the penalty coefficient of jth pollutant.
The environmental benefit of power purchase part (thermoelectricity) is taken into account herein, the disposal of pollutants data of each generation technology refer to text
Offer [28-29] (document 28：Zhang Xiaohui, man of virtue and ability roc reach, clock Jiaqing, wait to consider the power supply rule of Environmental costs and demand side management project
Draw model [J] electric power network techniques .2015,39 (10):2809-2814；Document 29：Qian Kejun, Yuan Yue, Shi Xiaodan, wait distributed
Environmental Effect Analysis [J] Proceedings of the CSEEs of power generation, 2008,28 (29):11-15.), the environmental value of each pollutant
Standard and the fine order of magnitude are as shown in table 1.
Table 1
4) sale of electricity cost is purchased
Purchase sale of electricity cost is represented by
In formula,Represent purchase sale of electricity power of the t moment micro-capacitance sensor to major network, be worth for it is negative when represent sale of electricity to major network, instead
Be power purchase；Represent the Spot Price of power grid.
In addition, the transferable load in Demand Side Response is user reduces itself electric cost under the excitation of Spot Price
From main response, without compensation, therefore, disregard DR cost of compensation.
1.2 constraints
1) active power balance constraint
In formula,Respectively each moment miniature combustion engine, energy storage, photovoltaic and wind turbine in real time go out
Power；Respectively predict load and t moment the load amount of producing only.
2) interconnection capacity-constrained
In formula,For dominant eigenvalues bound.
3) miniature combustion engine units limits and Climing constant
In formula,Represent that the attainable minimum and maximum of t moment miniature combustion engine institute is contributed respectively；Respectively miniature combustion engine minimum and maximum rated power；δdown、δupRespectively swash ratio of slope and lower climbing rate.
4) energy-storage system constrains
In charge and discharge process, the residual capacity of energy-storage system (energy storage system, ESS) usually uses lotus
Electricity condition (state of charge, SOC) characterizes, and the model and its constraints of storage battery SOC are represented by
SOCmin≤SOCt≤SOCmax (13)
In formula, ε is storage battery self-discharge rate；Δ t is time interval；Represent charge-discharge electric power,For electric discharge
State, is otherwise charged state；α is efficiency for charge-discharge；VESSFor storage battery total capacity.Energy storage system is represented respectively
The minimum and maximum charge-discharge electric power of system；SOCmax、SOCminThe respectively bound of SOC.
5) wind turbine and photovoltaic units limits
In formula,To abandon wind and abandoning luminous power；Respectively wind-powered electricity generation and photovoltaic is pre-
Measure power.
2 demand response layer Optimized model
Though conventional monolayers Optimized model can effectively reduce the operating cost of micro-capacitance sensor, it is difficult to optimization and coordinates clean energy resource
The indexs of correlation such as utilization rate, Demand-side peak-valley difference and controllable micro- source output stationarity, or even can be referred to because of economy with these
It is designated as cost.It is simple system further to be optimized by adjustment Demand-side load to improve the real-time matching that load is contributed with system
Degree, though effect makes moderate progress, regulating power is limited and larger to the factors influencing demand of Demand-side user.Therefore set forth herein based on
The demand response layer prioritization scheme of source lotus interaction, while micro-capacitance sensor operating cost is effectively reduced, coordinates and optimizes indices.
2.1 level-ones optimize
The cost of investment of honourable distributed regenerative resource (distributed renewable energy, DRE) compared with
Height, is difficult to obtain dispatching priority in traditional economy scheduling model, is unfavorable for the utilization of DRE, serious to hinder China's energy
The strategical reajustment of source consumption structure.To guide user power utilization Proactive traceback clean energy resource generated output, promote the height of clean energy resource
Effect utilization, improve proportion (such as document 30 of the clean energy resource in final energy consumption：Zeng Ming, Yang Yongqi, Liu Dunnan, wait
Energy internet " source-net-lotus-storage " coordinates and optimizes operation mode and key technology [J] electric power network techniques, 2016,40 (1):114-
124.) DR level-one prioritization schemes, are proposed, are contributed according to DR prediction curves and uncertainty DRE predictions transferable to DR at the same time negative
The sequential and DRE of lotus are contributed and are adjusted, so that load and DRE better adapt to mutual wave characteristic, realize that source lotus is interactive
Consumption.Its model is
In formula,Equivalent load after optimizing for level-one；Optimize the load amount of producing only for level-one；
The load for being respectively transferred to and producing in level-one optimization；NkRepresent transferable load species,For from when being transferred to t at the j moment
The unit number at quarter；PkFor the unit transfer amount of kth type load.
2.2 2-level optimization
Conventional distributed controllable micro- source such as miniature combustion engine, energy storage, which has the advantages that to contribute, stablizes, is adjustable, but load is a wide range of
Fluctuation not only increases system reserve capacity, it is also possible to makes miniature combustion engine when the fallback being in when contributing less or contributes excessive
Limit operation, frequently even start and stop, it is difficult to ensure the economy and electric network security of unit operation.Therefore, for formation more
For stable electric load operating status and the output stability in controllable micro- source is improved, set forth herein DR 2-level optimizations scheme.
Without considering under load condition, most preferably gone out using similar fitting process according to the history data of each conventional power unit
Force curve, and traffic department is set according to unit operation situation and equipment day repair schedule to its further revised gross capability
It is expected supply curve, 2-level optimization's scheme then optimizes load according to the curve, is adapted to load variations trend
Supply curve it is expected, so as to improve normal power supplies output stability.
Definition it is expected that supply curve isThen two degrees optimization model is
In formula,For the whole micro-grid system equivalent load after 2-level optimization；Turn only for 2-level optimization's Energy Load
Output；The load for respectively producing and being transferred in 2-level optimization's energy, it may be referred to level-one optimization energy
Source reference formula (19)~(21).
2-level optimization lays particular emphasis on the raising of normal power supplies output stability, though only load curve is adjusted, indirectly
The gross capability in the conventional controllable micro- source of microgrid layer is have adjusted, and the distribution of load power then depends on the warp of microgrid layer between each micro- source
Help Optimized Operation.
Maximum transfer capacity-constrained is in t moment optimizations at different levels
In formula,Respectively transferable load maximum is transferred to, produces capacity；It is excellent for Demand-side
Change the total load amount of producing only.
The two above stage is required to encourage user to adjust transferable load by Spot Price, and guiding user, which adjusts, to be used
Electricity plan makes to be conducive to electric power safety and economical production.The price elasticity of demand reflects sensitive journey of the workload demand to price change
Degree, its expression formula are
In formula, e is the price elasticity of demand；LbWith total load before Δ L respectively optimization and the load variations amount after optimization；
pbIt is respectively the adjustment amplitude of electricity price and electricity price before adjusting with Δ p.
Electricity price and the relation of Demand-side load are represented by (such as document 31：P.Thimmapuram,J.Kim,
A.Botterud,and Y.Nam,“Modeling and simulation of price elasticity of demand
using an agent-based model,”in Proc.IEEE Innovative Smart Grid Technologies
(ISGT),2010,2010,pp.1–8.)
L=ape (31)
In formula, a is constant；L is load；P is the Spot Price after optimization.Herein in literature research such as [7~9,31]
On the basis of perunit value form is used to load and Spot Price：
L*=La/Lb (32)
p*=p/pref (34)
In formula, L*、p*The respectively electricity price rate of expected load rate and Spot Price；La、LbRepresent the phase after certain time optimization
Hope the load before load and optimization；prefFor conventional electricity price (for definite value).
Although formula (25)~(29) constrain load transfer amount, thus constrain indirectly cool load translating ratio and in real time
Electricity price, but for further Spot Price is limited in reasonable interval, constraint p meets：
pmin≤p≤pmax (35)
In formula, pmax、pminConstrained for the bound of Spot Price.
Since DR is the subjective desire of user, its uncertainty can produce dispatching of power netwoks in certain influence (such as document 32：
Luo Chunjian, Li Yaowang, Xu Hanping, wait impact analysis [J] the electric system of demand responses uncertainty to Optimized Operation a few days ago
Automation, 2017 (5):22-29.), therefore uncertain factor R is introducedDExpected load rate is modified.It is revised negative
Lotus rateFor
Traffic department formulates rational electricity price according to expected load rate and the relation of combination Spot Price and Demand Side Response,
Encourage user to change consumption mode, reach the Expected Results to the adjustment of transferable load sequential.
3 model solution algorithms
GA (genetic algorithm) is a kind of parallel random search simulated nature genetic mechanism and theory of biological evolution and formed
Intelligent optimization algorithm, it is not necessary to which the knowledge of search space or other auxiliary informations, the iteration since trail, seeks global optimum
Solution.But it is long-term reluctant problem that GA local search abilities are weak, are also easy to produce precocious phenomenon.
The thermodynamic process of SA (simulated annealing) simulation high-temperature metal coolings, and use Metropolis criterion conducts
Search strategy, unconditionally accepts new high-quality solution, while poor new individual is made choice with certain probability again, avoids falling into
Enter local optimum, thus there is stronger local search ability.Metropolis criterions are
In formula, prFor the select probability of j individuals；xjFor current decision variable xiThe new decision variable produced；f(xj)、f(xi)
Respectively its corresponding fitness value；K is Boltzmann constants；T is annealing temperature.
Herein by f (xi) it is arranged to constant 0, and since three object functions of this paper schemes are nonnegative value, convolution
(37) can obtain：
From formula (38)：1) at the same temperature, fitness value is bigger, and the selected probability of the individual is smaller；2) repeatedly
In generation, is initial, and T is very high, and each selected probability difference very little of feasible solution, SA, which can be considered, is carrying out wide area search, avoids being absorbed in office
Portion is optimal；3) the iteration later stage, temperature is very low, even if the difference of the fitness value of each feasible solution only very little can also expand rapidly
High-quality individual and poor individual prGap, at this time SA can be considered carry out Local Search, in order to the optimal solution that becomes more meticulous.T is controlled
Solution procedure is made to carry out to the direction of optimal value.
It can be seen from the above that GA parallel searches provide search space as big as possible for SA, SA effectively increases the office of GA again
Portion's search capability, both, which combine, can obtain the efficient, Genetic Simulated Annealing Algorithm (SAGA) of strong robustness.
In solving model, each constraint is included in a manner of penalty in corresponding object function, is converted into without about
The Optimized model of beam.The flows of Algorithm for Solving this paper models is as shown in Figure 1, wherein, G, GmaxCurrent iteration number is represented respectively
And maximum iteration；M represents Population Size；τ represents annealing temperature decrement factor.Intersect and mutation probability depends on SA algorithms
Formula (38), but since mutation probability value is smaller, the Boltzmann constants k corresponding to it is less than in crossover probability
Boltzmann constants k.
Specific solution procedure is as follows：
A:According to each micro- source model of micro-grid system, all parameters of input model and constraints, select annealing temperature T, most
Big iterations GmaxAnd Population Size M；
B:Initialization level-one optimization population (feasible solution of each object function be the object function one by one
Body, all individual composition populations, optimization is that an optimal solution is found out in numerous feasible solutions), each individual of level-one optimization
Comprising data message：The electrical power P exported with the wind energy conversion system of periodwt,r, output power of photovoltaic module Ppv,r, level-one optimization in
The amount of the being transferred to P of loadLin,1With the amount of producing PLout,1；
C:Each individual that step B is produced brings formula (16) into and calculates target function value corresponding to it, due to this paper mesh
The individual of offer of tender numerical value minimum to be optimal, and the fitness value of individual be up to it is optimal, therefore in order to which solving result is consistent, by mesh
Offer of tender numerical value is inverted as each individual fitness value；
D：In population individual fitness value is ranked up from big to small using league matches selection, and retains optimum individual,
Then again other individuals are carried out with cross and variation to operate to obtain corresponding new individual, and is formed newly with the optimum individual of reservation
Population；
E:Then whether iterations G is more than maximum iteration G before judgingmax, as G is less than Gmax, then G=G+1, T=
T* τ, τ are annealing temperature decrement factor, then by step D-shaped into new population enter step C and carry out iterative cycles next time, directly
It is more than or equal to G to Gmax, output optimum individual (wherein Pwt,r、Ppv,rAnd PL,1Be optimum individual determine value substitute into 2-level optimization and
Final microgrid layer optimization), it is transferred to 2-level optimization.F：Similarly, the solution procedure of 2-level optimization and the optimization of microgrid layer is similar to one
Step B, C, D and E of level optimization, simply initialize each individual in population and include information difference, 2-level optimization and microgrid layer
The information that each individual of optimization is included is respectively PLin,2、PLout,2And Pess、Pgrid、Pμg, the result of output is respectively then Pcl、P*
And Pess、Pgrid、Pμg。
As can be seen that just successive step is carried out to load by the signified demand response mechanism of level-one optimization is allowed to main in Fig. 1
Dynamic tracking clean energy resource generated output, and by the P after optimizationwt,r、Ppv,rAnd PL,1It is stored in structure and supplies two as parameter
Level optimization is called；Similarly, 2-level optimization to the further adjustment of Demand-side load by improving load and controllable micro- source total phase
Hope and contributeMatching degree, determine the total transfer amount P of final loadcl, and thus calculate electricity price rate P*, optimize with level-one
Pwt,r、Ppv,rAs a result it is stored in structure and optimizes for microgrid layer economic load dispatching jointly；Last microgrid layer by economic goal function and
Corresponding constraints determines that (2-level optimization is most important to be for the economic load dispatching distribution contributed in each controllable micro- source such as miniature combustion engine, energy storage
Draw the total load transfer amount of demand response, the P optimized with reference to level-onewt,r、Ppv,rWith the output for drawing all traditional energy power supplys
The sum of, microgrid layer is that the general power that traditional energy needs to provide clearly is distributed to each tradition electricity according still further to economic load dispatching
Source).Red solid line with the arrow indicates the information transmission situation of each perfecting by stage in Fig. 1.
4 sample calculation analysis
1) model parameter and data
The micro-grid connection system formed in example using wind, light, gas, storage, includes wind park, photovoltaic plant and energy storage electricity
Stand each 1,2 groups of gas turbine, the parameter of various equipment is shown in Table 2.Use in sale of electricity cost model is purchased and formulated according to rate of load condensate
Spot Price, time scale 15min.
Table 2
4.2 interpretation of result
Based on above parameter, solution analysis carries out this paper models using improved SAGA algorithms first, verifies at different levels/layer
The feasibility and validity of optimization aim.Secondly four kinds of different schemes are contrasted respectively, to verify this programme to each index
Coordination optimization effect.
4.2.1 model solution
Demand Side Response point two-stage optimizes, input of the result that level-one optimizes as 2-level optimization, 2-level optimization
As a result the then input as the optimization of microgrid layer.The prediction gross capability of the clean energy resourcies such as level-one optimization foundation scene is to transferable load
Sequential adjustment is carried out, and determines the actual reference value that wind turbine and photovoltaic are contributed.SAGA algorithms are to wind turbine and photovoltaic generating optimization
The results are shown in Figure 2.Demand response layer load optimal at different levels such as Fig. 3.
From the honourable output feature and Demand-side level-one optimum results shown in Fig. 2 and Fig. 3, level-one optimization can lead to
Cross while change demand curve and the mode of clean energy resource supply curve this introduces a collection lotus interaction improves low-load period clean energy resource
Utilization rate and the load transfer amount for reducing clean energy resource output peak period.
In addition, from the figure 3, it may be seen that 2-level optimization more flattens out cunning than level-one optimization load curve, it is to improve to show 2-level optimization
Controllable micro- source output stability has made DR further optimization.
Spot Price (the rounding point that the expected load rate determined according to initial load and 2-level optimization's equivalent load is drawn
Moment) it is shown in Table 3.
Table 3
Traffic department can formulate rational electricity price according to the load curve of optimization and participate in demand side management to transfer user.It is aobvious
So, price elasticity of demand e and constant a is the optimized parameter determined in long-time investigation user is to the response pattern of Spot Price
Value.Spot Price in table 3 according to rate of load condensate formulation is easy to implement in rational section.
The optimum results that controllable micro- source is contributed in real time are as shown in Figure 4.As shown in Figure 4, energy-storage units are in night low electricity price area
Section is discharged from power grid power purchase when daytime, electricity price was higher, and electricity price peak period is contributed particularly evident but negative in 45~60 sections
The period that lotus is higher but electricity price is relatively low contributes and is obviously reduced, and illustrates in economic optimization is dispatched：1) influence energy storage and adjust work
It is that the load for determining Spot Price it is expected the size of the rate of transform rather than actual load, electricity price peak-valley difference is bigger, energy storage tune
Peak effect is more obvious；2) microgrid rationally can realize that indirect load shifts using the characteristic of energy-storage units.Further, since miniature combustion engine
1 unit power cost is higher than miniature combustion engine 2, and the output for preferentially increasing miniature combustion engine 2 in load growth is powered for system internal loading,
And when power grid unit power cost is more than miniature combustion engine, miniature combustion engine, which is contributed, substantially to be increased.
It can be seen from the above that Demand-side two-stage optimizing scheme proposed in this paper and Spot Price, which are developed programs, both can fully guide storage
The Peak Load Adjustment of itself can be played, and can effectively realize load peak load shifting, reduces cost.
The solving result of model is contrasted in addition, SAGA and tradition GA will be improved herein.To follow unitary variant
Principle and reliability, other operations of each algorithm are performed after population is initialized and are averaged to each 40 solving results of algorithm
Value is contrasted.Algorithm contrasts and is optimized to this respectively as shown in Fig. 5 and table 4.
Table 4
According to Fig. 5 and table 4, SAGA not only maintain GA algorithms it is simple and practicable the characteristics of, accelerate convergence rate,
And than the cost about 8.9% of GA Algorithm for Solving result reductions, the local search ability of GA is enhanced, improve the Shandong of algorithm
Rod and convergence precision.
4.2.2 scheme comparison
For the validity and feasibility of proof scheme, four kinds of contrast schemes are set up herein, wherein：
Scheme 1：Optimize containing only the individual layer of microgrid layer scheduling.
Scheme 2：Containing only the dual-layer optimization of DR first order optimization.Wherein DR level-ones optimum results determine AndThen three contributes other normal power supplies as the input that microgrid layer optimizes and optimizes.This programme lays particular emphasis on raising
Utilization of new energy resources rate, therefore include scheme comparison and verified.
Scheme 3：Containing only the dual-layer optimization of DR second level optimization.Since no level-one optimizes, load will be predictedWith
Scene prediction is contributedDirectly substitute the corresponding amount in formula (23) and withTogether as program 2-level optimization
Input quantity, and by optimum resultsAll micro- sources are contributed in input as the optimization of micro-capacitance sensor layer to be optimized.The program is received
Enter contrast to be intended to verify the simple influence for adding 2-level optimization to each index, the especially shadow to improving controllable micro- source stability
Ring.
Scheme 4：The dual-layer optimization of the two-stage response containing DR.It is intended to by coordination of contrast verification this paper schemes to each index
Optimization.
The simulation analysis of each index contrast of four kinds of schemes are as follows：
1) clean energy resource abandons electric rate
Under existing clean energy resource transfer efficiency, clean energy resource generating capacity is played to maximum, reduces clean energy resource
It is one of effective way for improving clean energy resource utilization rate to abandon electricity.Definition clean energy resource abandons electric rate and is：
In formula, rCSElectric rate is abandoned for clean energy resource；The prediction of respectively i-th kind clean energy resource t moment is contributed
Contribute with optimization.
Each scheme optimization such as Fig. 6, clean energy resource are abandoned electric rate and are seen.
Table 5
Quantitative analysis shows that two-stage optimizing can reduce DRE and abandon electric rate, and scheme four is abandoned electric rate than the synthesis of scheme one and reduced
10% or so.The effect that electric rate is abandoned in level-one optimization reduction is more notable than second level optimization.
2) controllable micro- source output riding index
Riding index expression formula is：
In formula, rstableFor riding index；Contribute for i-th kind of controllable micro- source optimization；For putting down in the sampling period
Contribute.
Each scheme optimum results such as Fig. 7, its riding index are shown in Table 6.
Table 6
From (39) formula, the micro- source of the smaller explanation of riding index goes out that fluctuation is smaller, is more conducive to the dimension in controllable micro- source
Shield.The comparing result of four kinds of schemes understands that 2-level optimization can effectively reduce riding index, improves the steady of controllable micro- source output
It is qualitative；The contrast of the riding index of scheme 3 and scheme 4 shows that level-one optimization has 2-level optimization's effect certain negative effect,
But still have clear improvement than scheme 1 and scheme 2, it was demonstrated that the validity of this paper schemes.
3) average load rate
Power system load fluctuation situation can be represented by average load rate
In formula, rloadFor average load rate；For the peak load after optimization；For being averaged for optimization afterload
Value.
Each scheme optimum results such as Fig. 8.Load fluctuation situation such as table 7.
Table 7
Fig. 8 and table 7 the results show that any level optimization of Demand-side prioritization scheme can effectively realize it is transferable negative
The sequential adjustment of lotus, demand response load is shifted from high electricity price section to low electricity price section, reaches the mesh of peak load shifting
's.Fig. 8 shows that scheme four not only reduces peak-valley difference, and improves average load rate, makes load curve more smooth.
4) each scheme cost
Each scheme is optimized to this to such as table 8.
Table 8
Each scheme is optimized to this display, proposed in this paper to be based on Demand-side two although clean energy resource cost of electricity-generating is higher
The micro-capacitance sensor dual-layer optimization scheme of level optimization (can especially substantially reduce clean energy resource and abandon electricity coordinating and optimizing each index
Amount) while still effectively reduce micro-capacitance sensor integrated operation cost.
5 conclusions
The integrated operation cost optimization scheduling model of micro-capacitance sensor is established herein；The two-stage optimizing model of DR is introduced at the same time,
And model is solved using improved SAGA algorithms, the results showed that：
1) genetic algorithm based on simulated annealing preferably improves the convergence rate and accuracy of GA.
2) method that Spot Price is formulated based on rate of load condensate is simple and direct rationally, easy to implement, and rational Spot Price can
Guiding energy-storage system gives full play to the Peak Load Adjustment of itself.
3) four kinds of scheme indices contrasts show that the optimization of DR level-ones can improve clean energy resource utilization rate, and 2-level optimization can
The riding index in controllable micro- source is reduced, the potentiality of demand response peak load shifting can be given full play to using two-stage optimizing, are being assisted
On the premise of tuning indices, the overall economic efficiency of microgrid operation is further improved.
DR and scene, the access of electric automobile distributed equipment, its uncertainty is to micro-capacitance sensor and energy source interconnection
The dynamic economic dispatch research of net brings opportunities and challenges, its rational dynamic response mechanism and Optimization Modeling still need to deeper
The exploration and research entered.
Claims (1)
1. a kind of grid-connected micro-capacitance sensor dual blank-holder based on two-stage demand response, it is characterised in that specifically include following step
Suddenly：
1), changed according to each micro- source model of micro-grid system, all parameters of input model and constraints, selected annealing temperature T, maximum
Generation number GmaxAnd Population Size M；
2) level-one optimization population, is initialized：Clean energy resource is selected to optimize the energy as level-one, collection level-one optimization multi-energy data is made
For optimization individual, level-one optimizes the data message that each individual includes：The electrical power P exported with the wind energy conversion system of periodwt,r, photovoltaic
Component output power Ppv,r, level-one optimization in load the amount of being transferred to PLin,1With the amount of producing PLout,1；
3), each individual that step 2) produces is brought into object functionCalculate it
Corresponding target function value, since the individual of target function value minimum is optimal, and the fitness value of individual be up to it is optimal,
It is in order to which solving result is consistent, target function value is inverted as each individual fitness value；
4), selected to being ranked up from big to small in step 2) population individual fitness value, and retained optimal using league matches
Individual, then again carries out other individuals cross and variations and operates to obtain corresponding new individual, and with the optimum individual of reservation
Form new population；
5) and then before judgement whether iterations G is more than maximum iteration Gmax, as G is less than Gmax, then G=G+1, T=T* τ,
τ is annealing temperature decrement factor, then the new population that step 4) is formed enters step the iterative cycles 3) carried out next time, until
G is more than or equal to Gmax, output optimum individual Pwt,r、Ppv,rAnd PL,12-level optimization is substituted into as definite value and final microgrid layer is excellent
Change, be transferred to 2-level optimization；
6) 2-level optimization population, is initialized：Iterations G and annealing temperature T is resetted, selection output is stablized, the adjustable energy is made
For 2-level optimization's energy, collection 2-level optimization multi-energy data is individual as an optimization, excellent according to each two level without considering under load condition
The history data for changing the energy obtains optimal output curve using similar fitting process, and sets traffic department according to 2-level optimization's energy
Further for revised gross capability it is expected supply curve, 2-level optimization is every to it for source operation conditions and equipment day repair schedule
Individual includes first best individual Pwt,r、Ppv,rAnd PL,1, and it is bent with each energy output of period 2-level optimization, expectation supply
Line；
7), each individual that step 6) produces is brought into object functionCalculate the target letter corresponding to it
Numerical value, whereinFor the micro-grid system equivalent load after 2-level optimization,It is expected to supply for micro-grid system, be due to target letter
The individual of numerical value minimum to be optimal, and the fitness value of individual be up to it is optimal, in order to which solving result is consistent, by target function value
It is inverted as each individual fitness value；
8), select to be ranked up in step 6) population individual fitness value from big to small using league matches, and retain optimal
Body, then again carries out other individuals cross and variations and operates to obtain corresponding new individual, and with the optimum individual structure of reservation
The population of Cheng Xin；
9) and then before judgement whether iterations G is more than maximum iteration Gmax, as G is less than Gmax, then G=G+1, T=T* τ,
τ is annealing temperature decrement factor, then the new population that step 8) is formed enters step the iterative cycles 7) carried out next time, until
G is more than or equal to Gmax, export each energy output, the total transfer amount P of load in optimum individual micro-grid systemcl, and thus calculate pair
Answer the Spot Price rate P of optimal solution*；
10) finally determine what the micro- source of each controllable 2-level optimization was contributed by micro-grid system economic goal function and corresponding constraints
Economic load dispatching is distributed.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120130556A1 (en) * | 2010-11-18 | 2012-05-24 | Marhoefer John J | Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization |
| US20120253567A1 (en) * | 2011-03-28 | 2012-10-04 | Levy Paul S | Method and process for acquiring and delivering electric vehicle owner-operator preference data which is used to schedule and regulate the charging of multiple electric vehicle batteries within a shared local power distribution network |
| US20130211988A1 (en) * | 2012-02-13 | 2013-08-15 | Accenture Global Services Limited | Electric vehicle distributed intelligence |
| CN104065060A (en) * | 2014-06-09 | 2014-09-24 | 徐多 | Independent micro-grid system double-layer economic dispatch optimization method |
| CN104779611A (en) * | 2015-03-23 | 2015-07-15 | 南京邮电大学 | Economic dispatch method for micro grid based on centralized and distributed double-layer optimization strategy |
| CN105787605A (en) * | 2016-03-24 | 2016-07-20 | 上海电力学院 | Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm |
| CN106372762A (en) * | 2016-10-12 | 2017-02-01 | 国网上海市电力公司 | Microgrid economic optimal operation design method with demand response included |
| CN107294212A (en) * | 2017-07-25 | 2017-10-24 | 山东大学 | Consider the microgrid dual-layer optimization dispatching method and system of different air conditioner load characteristics |
| CN107508284A (en) * | 2017-08-15 | 2017-12-22 | 华北电力大学 | The micro-capacitance sensor distributed optimization dispatching method of meter and electrical interconnection |
-
2018
- 2018-01-03 CN CN201810004691.0A patent/CN108009693B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120130556A1 (en) * | 2010-11-18 | 2012-05-24 | Marhoefer John J | Virtual power plant system and method incorporating renewal energy, storage and scalable value-based optimization |
| US20120253567A1 (en) * | 2011-03-28 | 2012-10-04 | Levy Paul S | Method and process for acquiring and delivering electric vehicle owner-operator preference data which is used to schedule and regulate the charging of multiple electric vehicle batteries within a shared local power distribution network |
| US20130211988A1 (en) * | 2012-02-13 | 2013-08-15 | Accenture Global Services Limited | Electric vehicle distributed intelligence |
| CN104065060A (en) * | 2014-06-09 | 2014-09-24 | 徐多 | Independent micro-grid system double-layer economic dispatch optimization method |
| CN104779611A (en) * | 2015-03-23 | 2015-07-15 | 南京邮电大学 | Economic dispatch method for micro grid based on centralized and distributed double-layer optimization strategy |
| CN105787605A (en) * | 2016-03-24 | 2016-07-20 | 上海电力学院 | Micro-grid economic and optimal operation and scheduling method based on improved quantum genetic algorithm |
| CN106372762A (en) * | 2016-10-12 | 2017-02-01 | 国网上海市电力公司 | Microgrid economic optimal operation design method with demand response included |
| CN107294212A (en) * | 2017-07-25 | 2017-10-24 | 山东大学 | Consider the microgrid dual-layer optimization dispatching method and system of different air conditioner load characteristics |
| CN107508284A (en) * | 2017-08-15 | 2017-12-22 | 华北电力大学 | The micro-capacitance sensor distributed optimization dispatching method of meter and electrical interconnection |
Non-Patent Citations (2)
| Title |
|---|
| 刘方 等: ""微电网经济优化运行研究综述"", 《电器与能效管理技术》 * |
| 李小荣: ""基于双层优化的微电网系统规划设计方法初探"", 《建材与装饰》 * |
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