CN105896580B - A kind of micro-capacitance sensor multiobjective optimization control method and device - Google Patents
A kind of micro-capacitance sensor multiobjective optimization control method and device Download PDFInfo
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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
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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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
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- 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
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- 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
Abstract
The present invention provides a kind of micro-capacitance sensor multiobjective optimization control methods, initially set up the objective function of micro-capacitance sensor optimization, wherein objective function includes at least the electricity charge and minimizes objective function and cell health state function；Then the constraint of energy-storage battery charge-discharge electric power, charge and discharge count constraint and the constraint of total storing electricity of the micro-capacitance sensor optimization are determined；The objective function is solved by particle swarm algorithm again, obtains the optimal solution and most inferior solution of each objective function；The weight coefficient of each objective function is obtained finally by linear weighted function summation, obtains the optimal solution of the micro-capacitance sensor multiobjective optimal control.As it can be seen that this programme carries out multiple-objection optimization to energy-storage system state-of-charge and service life, performance driving economy and the energy storage service life of micro-capacitance sensor can be effectively improved.
Description
Technical field
The present invention relates to network optimization technical field more particularly to a kind of micro-capacitance sensor multiobjective optimization control methods and dress
It sets.
Background technique
With the continuous expansion of power grid scale, traditional centralized power generation and the power grid construction mode of long distance power transmission are shown
More and more limitations.On the other hand, petering out with conventional fossil energy, the renewable energy power generation of clean and effective
Technology has received widespread attention.Micro-capacitance sensor is one of important form of distributed power generation, both can with bulk power grid parallel running,
Local load can independently be, electric energy is provided, help to solve the problems, such as that bulk power grid encounters various, be the weight of smart grid construction
Want component part.And the optimization operation of micro-capacitance sensor is that the optimization of overall cost is realized while meeting workload demand, is micro-
The key that electrical network economy benefit emerges from.Therefore, the optimization operation problem for studying micro-capacitance sensor is most important.
Currently, micro-capacitance sensor optimization aim is relatively simple, and does not consider individually for mixed energy storage system, therefore it provides one
Kind micro-capacitance sensor multiobjective optimization control method is current technical problem urgently to be resolved.
Summary of the invention
The present invention provides a kind of micro-capacitance sensor multiobjective optimization control methods, to energy-storage system state-of-charge and service life
Multiple-objection optimization is carried out, performance driving economy and the energy storage service life of micro-capacitance sensor can be effectively improved.
The present invention provides a kind of micro-capacitance sensor multiobjective optimization control methods, comprising:
The objective function of micro-capacitance sensor optimization is established, the objective function includes at least the electricity charge and minimizes objective function and electricity
Pond health status function；
Determine the constraint of energy-storage battery charge-discharge electric power, charge and discharge count constraint and the total storage electricity of the micro-capacitance sensor optimization
Amount constraint；
The objective function is solved by particle swarm algorithm, obtains the optimal solution of each objective function and most bad
Solution；
The weight coefficient of each objective function is obtained by linear weighted function summation, obtains the micro-capacitance sensor multiple target
The optimal solution of optimal control.
Preferably, the electricity charge minimize objective function are as follows:
Wherein, c (t) is the Spot Price of each period, and Δ t is the time interval of each period, and T is dispatching cycle,
Pline(t) dominant eigenvalues between micro-capacitance sensor and bulk power grid.
Preferably, the cell health state function are as follows:
Wherein, MESFor the set of energy storage device, the energy storage device includes sodium-sulphur battery and lithium battery；λk tIt is set for energy storage
Standby charge and discharge maintenance expense equivalent coefficient of the l in moment t, PES,l tFor energy storage device l moment t charge-discharge electric power, it is described to fill
Discharge power is positive value, and Δ t is the time interval of each period, fcycle,k tFor energy storage device l moment t the equivalent energy storage service life
Cost depletions.
Preferably, the energy-storage battery charge-discharge electric power constraint are as follows:
0 < PNas(t) < PrateNpulse(t)
abs[Pl(t)-Pl(t-1)] < Pset
Wherein, PNasIt (t) is sodium-sulphur battery output power, PrateFor sodium-sulphur battery rated output power, PsetFor power change
Change limit value, NpulseIt (t) is t moment sodium sulphur pulse limit: 0.8 < Npulse(t) 1 <.
Preferably, the charge and discharge count constraint are as follows:
Wherein, t0For the initial time of dispatching cycle, k is nonnegative integer, and δ is time interval, and enables T=N dispatching cycleT
δ, NTFor positive integer；Charge and discharge number α1、α2For definite value, udisIt (t) is sodium-sulfur battery energy storage system discharge state, uchIt (t) is sodium
Sulphur battery energy storage system maximum charge power charged state, usup-chBe (t) sodium-sulfur battery energy storage system half-power charged state,
ustandby(t) it is sodium-sulfur battery energy storage system reserve state, meets between each state:
ustandby(t),udis(t),uch(t),usup-ch(t)∈{0,1}
ustandby(t)+udis(t)+uch(t)+usup-ch(t)=1
Preferably, described to include: to objective function solution by particle swarm algorithm
Initialize particle populations；
The optimal solution in the particle populations is obtained, the fitness of each particle populations is calculated；
Update speed and the position of the particle populations；Until reaching default iteration upper limit value.
Preferably, described that the weight coefficient of each objective function is obtained by linear weighted function summation, it obtains described
The optimal solution of micro-capacitance sensor multiobjective optimal control, comprising:
Ranking scale matrix is obtained according to paried comparison algorithm, calculates the sum of each row element of the ranking scale matrix,
By arrive greatly it is small successively sort, obtain index importance sequence；
Determine weights omega=[ω of each index1,ω2]T, the optimal solution are as follows:
Wherein, FiIt (x) is objective function fiSubordinating degree function:
Wherein, fi,maxIt is using particle swarm algorithm to objective function fiCarry out optimal solution when single object optimization；fi,minIt is
Using particle swarm algorithm to objective function fiCarry out most inferior solution when single object optimization；I=1,2.
A kind of micro-capacitance sensor multiobjective optimal control device, comprising:
Modeling module, for establishing the objective function of micro-capacitance sensor optimization, the objective function is minimized including at least the electricity charge
Objective function and cell health state function；
Determining module, the constraint of energy-storage battery charge-discharge electric power, charge and discharge number for determining the micro-capacitance sensor optimization are about
Beam and the constraint of total storing electricity；
Module is obtained, for solving by particle swarm algorithm to the objective function, obtains each objective function
Optimal solution and most inferior solution；
Computing module obtains institute for obtaining the weight coefficient of each objective function by linear weighted function summation
State the optimal solution of micro-capacitance sensor multiobjective optimal control.
By above scheme it is found that initially setting up micro- electricity the present invention provides a kind of micro-capacitance sensor multiobjective optimization control method
The objective function of network optimization, wherein objective function includes at least the electricity charge and minimizes objective function and cell health state function；
Then the constraint of energy-storage battery charge-discharge electric power, charge and discharge count constraint and the total storing electricity of the micro-capacitance sensor optimization are determined about
Beam；The objective function is solved by particle swarm algorithm again, obtains the optimal solution and most inferior solution of each objective function；Most
The weight coefficient of each objective function is obtained by linear weighted function summation afterwards, obtains the micro-capacitance sensor multiple-objection optimization control
The optimal solution of system.As it can be seen that this programme carries out multiple-objection optimization to energy-storage system state-of-charge and service life, can effectively improve micro-
The performance driving economy of power grid and energy storage service life.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is a kind of flow chart of micro-capacitance sensor multiobjective optimization control method provided in an embodiment of the present invention；
Fig. 2 be a kind of photovoltaic provided in an embodiment of the present invention go out force data, load data, power shortage data curve
Figure；
Fig. 3 is a kind of curve graph of local tou power price data provided in an embodiment of the present invention；
Fig. 4 is the curve graph of the dominant eigenvalues after a kind of optimization provided in an embodiment of the present invention；
Fig. 5 is a kind of structural schematic diagram of micro-capacitance sensor multiobjective optimal control device provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, be a kind of micro-capacitance sensor multiobjective optimization control method provided in an embodiment of the present invention, comprising steps of
S1: establish micro-capacitance sensor optimization objective function, the objective function include at least the electricity charge minimize objective function with
And cell health state function；
S2: the energy-storage battery charge-discharge electric power constraint of micro-capacitance sensor optimization, charge and discharge count constraint are determined and is always deposited
Reserve of electricity constraint；
S3: solving the objective function by particle swarm algorithm, obtains the optimal solution and most of each objective function
Inferior solution；
S4: the weight coefficient of each objective function is obtained by linear weighted function summation, it is more to obtain the micro-capacitance sensor
The optimal solution of objective optimization control.
Wherein, the electricity charge minimize objective function are as follows:
Wherein, c (t) is the Spot Price of each period, and Δ t is the time interval of each period, and T is dispatching cycle,
Pline(t) dominant eigenvalues between micro-capacitance sensor and bulk power grid.
The cell health state function are as follows:
Wherein, MESFor the set of energy storage device, the energy storage device includes sodium-sulphur battery and lithium battery；λk tIt is set for energy storage
Standby charge and discharge maintenance expense equivalent coefficient of the l in moment t, PES,l tFor energy storage device l moment t charge-discharge electric power, it is described to fill
Discharge power is positive value, and Δ t is the time interval of each period, fcycle,k tFor energy storage device l moment t the equivalent energy storage service life
Cost depletions.
The energy-storage battery charge-discharge electric power constraint are as follows:
0 < PNas(t) < PrateNpulse(t)
abs[Pl(t)-Pl(t-1)] < Pset
Wherein, PNasIt (t) is sodium-sulphur battery output power, PrateFor sodium-sulphur battery rated output power, PsetFor power change
Change limit value, NpulseIt (t) is t moment sodium sulphur pulse limit: 0.8 < Npulse(t) 1 <.
The charge and discharge count constraint are as follows:
Wherein, t0For the initial time of dispatching cycle, k is nonnegative integer, and δ is time interval, and enables T=N dispatching cycleT
δ, NTFor positive integer；Charge and discharge number α1、α2For definite value, udisIt (t) is sodium-sulfur battery energy storage system discharge state, uchIt (t) is sodium
Sulphur battery energy storage system maximum charge power charged state, usup-chBe (t) sodium-sulfur battery energy storage system half-power charged state,
ustandby(t) it is sodium-sulfur battery energy storage system reserve state, meets between each state:
ustandby(t),udis(t),uch(t),usup-ch(t)∈{0,1}
ustandby(t)+udis(t)+uch(t)+usup-ch(t)=1
Specifically, described include: to objective function solution by particle swarm algorithm
Initialize particle populations；
The optimal solution in the particle populations is obtained, the fitness of each particle populations is calculated；
Update speed and the position of the particle populations；Until reaching default iteration upper limit value.
Specifically, described obtain the weight coefficient of each objective function by linear weighted function summation, obtain described
The optimal solution of micro-capacitance sensor multiobjective optimal control, comprising:
Ranking scale matrix is obtained according to paried comparison algorithm, calculates the sum of each row element of the ranking scale matrix,
By arrive greatly it is small successively sort, obtain index importance sequence；
Determine weights omega=[ω of each index1,ω2]T, the optimal solution are as follows:
Wherein, FiIt (x) is objective function fiSubordinating degree function:
Wherein, fi,maxIt is using particle swarm algorithm to objective function fiCarry out optimal solution when single object optimization；fi,minIt is
Using particle swarm algorithm to objective function fiCarry out most inferior solution when single object optimization；I=1,2.
Be illustrated by taking a micro-capacitance sensor as an example, method flow diagram as shown in Figure 1, scheduling in the micro-capacitance sensor every 15 minutes is primary,
One day 24 hour, total 24*4=96 dispatching point, t={ 1,2,3 ..., 96 }.Photovoltaic goes out force data, load data, power
Vacancy data are as shown in Figure 2.
The charge-discharge electric power of every time of sodium-sulphur battery battery is set no more than maximum permissible value ± PNasmax, wherein
PNasmax=20kw.Negative value indicates sodium-sulphur battery electric discharge.The charge-discharge electric power of every time of lithium battery is set no more than maximum
Permissible value ± PLimax, wherein PLimax=20kw, negative value indicate lithium battery electric discharge.Local tou power price data are as shown in Figure 3.
Dominant eigenvalues after optimization are as shown in figure 4, the electricity charge at this time are -373.3985 yuan, and micro-capacitance sensor is according to peak valley at this time
Electricity price rationally adjusts energy storage power output, in the electricity price peak period, energy storage device electric discharge, and in the electricity price paddy period, energy storage device charging, thus
Improve its economical operation benefit.
The common adjustment effect of hybrid energy-storing can avoid the frequent charge and discharge of lithium battery, improve service life of lithium battery；Meanwhile sodium sulphur
Battery allow high charge-discharge number, fast charge-discharge velocity characteristic can effectively smooth dominant eigenvalues, reduce distributed generation resource pair
The impact of bulk power grid.
Except this, as shown in figure 5, the present embodiment additionally provides a kind of micro-capacitance sensor multiobjective optimal control device, comprising:
Modeling module 11, for establishing the objective function of micro-capacitance sensor optimization, it is minimum that the objective function includes at least the electricity charge
Change objective function and cell health state function；
Determining module 12, for determining the constraint of energy-storage battery charge-discharge electric power, the charge and discharge number of the micro-capacitance sensor optimization
Constraint and total storing electricity constraint；
Module 13 is obtained, for solving by particle swarm algorithm to the objective function, obtains each objective function
Optimal solution and most inferior solution；
Computing module 14 is obtained for obtaining the weight coefficient of each objective function by linear weighted function summation
The optimal solution of the micro-capacitance sensor multiobjective optimal control.
Its working principle is referring to embodiment of the method.
In conclusion initially setting up micro-capacitance sensor optimization the present invention provides a kind of micro-capacitance sensor multiobjective optimization control method
Objective function, wherein objective function includes at least the electricity charge and minimizes objective function and cell health state function；Then really
The constraint of energy-storage battery charge-discharge electric power, charge and discharge count constraint and the constraint of total storing electricity of the fixed micro-capacitance sensor optimization；Again
The objective function is solved by particle swarm algorithm, obtains the optimal solution and most inferior solution of each objective function；Finally lead to
It crosses linear weighted function summation and obtains the weight coefficient of each objective function, obtain the micro-capacitance sensor multiobjective optimal control
Optimal solution.As it can be seen that this programme carries out multiple-objection optimization to energy-storage system state-of-charge and service life, micro-capacitance sensor can be effectively improved
Performance driving economy and the energy storage service life.
If function described in the present embodiment method is realized in the form of SFU software functional unit and as independent product pin
It sells or in use, can store in a storage medium readable by a compute device.Based on this understanding, the embodiment of the present invention
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, this is soft
Part product is stored in a storage medium, including some instructions are used so that calculating equipment (it can be personal computer,
Server, mobile computing device or network equipment etc.) execute all or part of step of each embodiment the method for the present invention
Suddenly.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), deposits at random
The various media that can store program code such as access to memory (RAM, Random Access Memory), magnetic or disk.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (7)
1. a kind of micro-capacitance sensor multiobjective optimization control method characterized by comprising
The objective function of micro-capacitance sensor optimization is established, the objective function includes at least electricity charge minimum objective function and battery is strong
Health function of state；
Determine the constraint of energy-storage battery charge-discharge electric power, charge and discharge count constraint and the total storing electricity of the micro-capacitance sensor optimization about
Beam；
The objective function is solved by particle swarm algorithm, obtains the optimal solution and most inferior solution of each objective function；
Ranking scale matrix is obtained according to paried comparison algorithm, calculates the sum of each row element of the ranking scale matrix, by big
It successively sorts to small, obtains index importance sequence；
Determine weights omega=[ω of each index1,ω2]T, the optimal solution are as follows:
Wherein, FiIt (x) is objective function fiSubordinating degree function:
Wherein, fi,maxIt is using particle swarm algorithm to objective function fiCarry out optimal solution when single object optimization；fi,minIt is to utilize
Particle swarm algorithm is to objective function fiCarry out most inferior solution when single object optimization；I=1,2.
2. micro-capacitance sensor multiobjective optimization control method according to claim 1, which is characterized in that the electricity charge minimize mesh
Scalar functions are as follows:
Wherein, c (t) is the Spot Price of each period, and Δ t is the time interval of each period, and T is dispatching cycle, Pline(t)
Dominant eigenvalues between micro-capacitance sensor and bulk power grid.
3. micro-capacitance sensor multiobjective optimization control method according to claim 1, which is characterized in that the cell health state
Function are as follows:
Wherein, MESFor the set of energy storage device, the energy storage device includes sodium-sulphur battery and lithium battery；For energy storage device l when
Carve the charge and discharge maintenance expense equivalent coefficient of t, PES,l tCharge-discharge electric power for energy storage device l in moment t, the charge-discharge electric power
For positive value, Δ t is the time interval of each period,For energy storage device l moment t equivalent energy storage life consumption cost.
4. micro-capacitance sensor multiobjective optimization control method according to claim 1, which is characterized in that the energy-storage battery charge and discharge
Electrical power constraint are as follows:
0 < PNas(t) < PrateNpulse(t)
abs[Pl(t)-Pl(t-1)] < Pset
Wherein, PNasIt (t) is sodium-sulphur battery output power, PrateFor sodium-sulphur battery rated output power, PsetFor changed power restriction
Value, NpulseIt (t) is t moment sodium sulphur pulse limit: 0.8 < Npulse(t) 1, P <lIt (t) is power of the energy storage device l in t moment,
PlIt (t-1) is power of the energy storage device l at the t-1 moment.
5. micro-capacitance sensor multiobjective optimization control method according to claim 1, which is characterized in that the charge and discharge number is about
Beam are as follows:
Wherein, t0For the initial time of dispatching cycle, k is nonnegative integer, and δ is time interval, and enables T=N dispatching cycleTδ, NT
For positive integer；Charge and discharge number α1、α2For definite value, udisIt (t) is sodium-sulfur battery energy storage system discharge state, uchIt (t) is sodium sulphur electricity
Pond energy-storage system maximum charge power charged state, usup-chBe (t) sodium-sulfur battery energy storage system half-power charged state,
ustandby(t) it is sodium-sulfur battery energy storage system reserve state, meets between each state:
ustandby(t),udis(t),uch(t),usup-ch(t)∈{0,1}
ustandby(t)+udis(t)+uch(t)+usup-ch(t)=1.
6. micro-capacitance sensor multiobjective optimization control method according to claim 1, which is characterized in that described to be calculated by population
Method solves the objective function
Initialize particle populations；
The optimal solution in the particle populations is obtained, the fitness of each particle populations is calculated；
Update speed and the position of the particle populations；Until reaching default iteration upper limit value.
7. a kind of micro-capacitance sensor multiobjective optimal control device characterized by comprising
Modeling module, for establishing the objective function of micro-capacitance sensor optimization, the objective function includes at least the electricity charge and minimizes target
Function and cell health state function；
Determining module, for determine the constraint of energy-storage battery charge-discharge electric power, the charge and discharge count constraint of micro-capacitance sensor optimization with
And total storing electricity constraint；
Module is obtained, for solving by particle swarm algorithm to the objective function, obtains the optimal of each objective function
Solution and most inferior solution；
Computing module calculates each row of the ranking scale matrix for obtaining ranking scale matrix according to paried comparison algorithm
The sum of element, by arrive greatly it is small successively sort, obtain index importance sequence；
Determine weights omega=[ω of each index1,ω2]T, the optimal solution are as follows:
Wherein, FiIt (x) is objective function fiSubordinating degree function:
Wherein, fi,maxIt is using particle swarm algorithm to objective function fiCarry out optimal solution when single object optimization；fi,minIt is to utilize
Particle swarm algorithm is to objective function fiCarry out most inferior solution when single object optimization；I=1,2.
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