CN106911139A - The energy-optimised management methods of super capacitor RTG based on genetic algorithm - Google Patents
The energy-optimised management methods of super capacitor RTG based on genetic algorithm Download PDFInfo
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- CN106911139A CN106911139A CN201710245398.9A CN201710245398A CN106911139A CN 106911139 A CN106911139 A CN 106911139A CN 201710245398 A CN201710245398 A CN 201710245398A CN 106911139 A CN106911139 A CN 106911139A
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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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
- H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
-
- 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 is optimized in the case where system energy consumption and non-renewable energy is considered to overall hybrid power RTG system capacities management, obtains the optimal power output of the diesel generating set and ultracapacitor group under system entirety energy consumption minimum.Propose a kind of super capacitor RTG energy management methods based on genetic algorithm.In the case where hybrid power system energy consumption and non-renewable capacity factor is considered, the Mathematical Modeling of hybrid power system is set up by the characterisitic parameter of diesel generating set, ultracapacitor group and loading demand, provide object function and constraints, last genetic Optimization Algorithm is solved to function, draws the optimal power output of generating set and capacitor bank.Super capacitor RTG energy management method of the present invention based on genetic algorithm, is applied to hybrid power RTG systems, and the hybrid power RTG systems include diesel generating set, ultracapacitor group and load motor.
Description
Technical field
The present invention relates to crane hybrid power system energy management, and in particular to a kind of super electricity based on genetic algorithm
Hold the energy-optimised management methods of RTG.
Background technology
At present, it is that main energy is originated that China's ports handling machine is mainly with diesel generating set, in order to reduce biography
Fuel consumption and discharge that the large-sized diesel generating set of system is caused, domestic and foreign scholars propose ultracapacitor, flywheel and lithium
Battery reduces the scale of diesel generating set as power buffer.Wherein, the energy density of lithium battery is high, but
Efficiency is low during high current charge-discharge, and its battery life is not long, expensive.The energy density of ultracapacitor is low, but power is close
Degree is high, and does not have any electrochemical reaction in charge and discharge process, can quickly absorb and release energy, efficiency high,
Long lifespan.When motor Accelerating running, peak power requirements time are relatively short, ultracapacitor can provide its peak work of short time
Rate.And flywheel is similar to super capacitor characteristics, but its self-discharge rate is high.
Energy management strategies for RTG, are totally divided into two kinds, and one kind is rule-based strategy, and another kind is to be based on
The strategy of optimization.
2006, South Korea Sang-Min Kim and Seung-Ki Sul have studied the energy of super capacitor hybrid electric RTG
Buret is managed and control strategy problem, is controlled using traditional PI, and rate of economizing gasoline is 35%, and the discharge capacity of diesel engine unit waste gas is reduced
More than 40%.Between 2010-2012, a series of papers that French Petar J.Grbovic are delivered, to based on ultracapacitor
Power-driven system control strategy and three-level DC converter have carried out system research, have reached crane system and have been made in regeneration
The uninterrupted operation of dynamic process.The work of the two people is all based on the energy management control strategy of rule.
The hybrid power RTG power-supply systems (Hybrid System) of SUMITOMO CHEMICAL heavy industry research and development are using lithium battery as energy storage
Device.The power output of battery is substantially at stable state in the application, and charge-discharge velocity is high, improves the circulation longevity of lithium battery
Life.The experimental data of Sumitomo Heavy Industries Ltd is demonstrated, and the cycle life of the lithium battery can have more than 7 years, largely improve
The inferior position that service life of lithium battery is not grown.RTG diesel generating sets and lithium battery hybrid power supply technology are entered in multiple container terminals
Go trial operation, and achieve certain effect.In October, 2006, in August, 2007 was in Hong Kong world packaging at Japanese Guia Hill port
Box terminal (HIT), in Japanese big black container terminal, in May, 2008 is in Shenzhen Yantian trans-container harbour point within 2 months 2008
Performance test has not been carried out, wherein by taking a hybrid power RTG of Hong Kong harbour as an example, loading demand general power is 370kW, lithium
The power output difference 270KW and 130KW of battery pack and diesel generator sets, relative to conventional crane system, diesel generator sets are matched somebody with somebody
The power put reduces nearly 65%, and black smoke phenomenon is significantly reduced.At home in terms of research, Zhenhua heavy industry have developed lithium battery RTG
Model machine, experiment shows that its energy-saving effect is notable.These work are also based on rule.
And Stefano Pietrosanti, William Holderbaum and Victor M.Becerra propose band and fly
The optimal management strategy of energy stores in the case of wheel, the optimization which solved under container hoisting time random case is asked
Topic.The work of Yoash Levron and Doron Shmilovitz is also based on optimisation strategy, but pair as if fuel cell and
Mobile phone, does not account for the transmission system with regeneration energy.
Still further aspect, most of scientific research scholar is for hybrid power RTG energy managements just for diesel-driven generator
The carrying out of group and ultracapacitor group or lithium battery group individually controls, and is integrally accounted for rather than from system.
The content of the invention
For the problem that above-mentioned present situation and correlation technique are present, the present invention is considering system energy consumption and non-renewable energy
In the case of, overall hybrid power system energy management is optimized, obtain the diesel generation under system entirety energy consumption minimum
The optimal power output of unit and ultracapacitor group.A kind of super capacitor RTG energy based on genetic algorithm is proposed to this
Management method.According to designed mixed power system structure block diagram, it is proposed that consider hybrid power system energy consumption and non-renewable
Under capacity factor, hybrid power system is set up by the characterisitic parameter of diesel generating set, ultracapacitor group and loading demand
Mathematical Modeling, provide object function and constraints, last genetic Optimization Algorithm is solved to function, draws generating set
With the optimal power output of capacitor bank.
Technical scheme is as follows：
A kind of super capacitor RTG energy management methods based on genetic algorithm, are applied to hybrid power RTG systems, described
Hybrid power RTG systems include diesel generating set, ultracapacitor group and load motor.Load motor is hybrid power RTG
Lifting mechanism.Diesel generating set connects through DC (direct current) busbars and gives load motor energy supply, ultracapacitor with rectifier
Group is connected with two-way DC/DC converters and be parallel to again DC busbars, and when lifting mechanism driving load rises, ultracapacitor group is used
To provide peak power, when lifting mechanism declines, ultracapacitor group absorption and regeneration power energy storage.
The super capacitor RTG energy management methods based on genetic algorithm are comprised the following steps：
Step one, initially set up the Mathematical Modeling on diesel generating set.Disappeared according to typical diesel-driven generator fuel
The fuel consumption that consumption curve map can obtain diesel-driven generator is normally approximately the quadratic function related to generator power.
Wi f=ai(Pi E)2+biPi E+ci (1)
Wherein, Pi EIt is the power output of generator, is equal to Pi E(t), Pi ET () is that i-th generator is produced in t
Raw power, and meetai、biAnd ciIt is constant, its value is determined by other specifications such as types.Therefore, mix
Diesel generating set form of the fuel consumption that motor is produced in [0, T Δ t] time range is in closing power RTG systems：
Wherein, EICEIt is the energy of generating set fuel consumption, Pi EratedIt is the rated output power of engine, T is mixing
One complete cycle of operation of power RTG systems, H is diesel oil calorific value.
Step 2, Mathematical Modeling of the foundation on ultracapacitor group.According to hybrid power RTG systems be in feed, again
When raw braking and standby these three states, corresponding ultracapacitor group be respectively necessary in hybrid power RTG operating process to
Load provides energy, absorption and regeneration energy stores and cluster engine and gives capacitor bank charging energy supply, obtains
Wherein, ESCIt is the energy that capacitor bank is produced, PCT power that () provides for ultracapacitor group in t is while be also
System returns to the power that the power or ultracapacitor group of ultracapacitor group are provided to load, can just can bear.Ought be
When system is in feed condition, i.e., capacitor bank gives system energy supply, now PC(t) ＜ 0；When system is in regenerative braking state or treats
During machine state, i.e., system charges to capacitor bank, now PC(t) ＞ 0.
Step 3, after the Mathematical Modeling for setting up diesel generating set and ultracapacitor group, it is necessary to according to hybrid power
The relation of system capacity sets up the Mathematical Modeling of non-renewable energy.According in actual mechanical process, the energy that capacitor bank absorbs
It is limited, because total system energy consumption, non-renewable energy can still be produced.In order to reduce the generation of non-renewable energy, it is considered to
Difference between generating set and the overall energy supply of ultracapacitor group and loading demand, the non-renewable cost of energy letter for drawing
Number is as follows：
Wherein, ENon-reIt is non-renewable energy, PLT () is the demand power in t load, in the present invention load point
Cloth is known.
Step 4, object function is listed according to goal of the invention：
J=EICE+λ×ESC+γ×ENon-re (5)
Wherein, λ and γ are constant-weights, represent ratio point of the overall energy consumption in capacitor bank and non-renewable energy
Match somebody with somebody.
Step 5, constraint, electricity according to the object function listed, the constraints of each variable of restriction, including generating set
Container group must be constrained and constrained with load power demand：
1) constraint of generating set：In order that diesel-driven generator keeps a relatively low specific fuel consumption, it is by operation model
It is have a range of to enclose, and its power limit is Pi Emin≤Pi E(k)≤Pi Emax, wherein Pi EminAnd Pi EmaxRespectively i-th hair
The minimum and maximum power of motivation output.
2) constraint of capacitor bank：According to the ultracapacitor operation manual that factory provides, in order to maintain ultracapacitor
Life-span, therefore admissible discharge and recharge rate scope be-PChmax≤PC(k)≤PDChmax, wherein PChmaxAnd PDChmaxIt is respectively fair
Perhaps maximum charge and discharge power.
3) load power demand constraint：Ensure that hybrid power RTG systems can meet the power demand of load, therefore PE
(k)+PC(k)≥Pd(k)。
Step 6, after object function and constraints is given, with target of the genetic algorithm for solving on cost
Function, so as to obtain the diesel generating set of super capacitance hybrid power RTG and the optimal power output of ultracapacitor group；Tool
Body step is as follows：
1)：Individual UVR exposure, produces initial population；Super capacitance hybrid power RTG systems a complete cycle it is negative
Load demand is up-sampled, and sampling number n tries one's best and obeys loading demand curve and be uniformly distributed, by the corresponding load need of n sampled point
Evaluation brings the object function of step 4 into, obtains the target letter of the n variable on generating set power and capacitor bank power
Number, while the constraints according to step 5 carries out gene code to this n variable using unsigned binary number, coding is conciliate
Coded program is mutually changed, and the size value of population size is m, and m values between 40-100, i.e. colony are made up of m individuality, often
It is individual to be produced by random device；
2)：Calculate fitness；According to step 4, on hybrid power RTG energy ezpenditure object function value non-negative, and
It in the hope of functional minimum value is optimization aim to be, therefore directly by the use of target function value as the fitness of the individuality of population m；
3)：Judge whether to meet Optimality Criteria；It is non-when the fitness that hybrid power RTG object functions calculate is brought into
Negative is considered as optimized individual, and the result for drawing is the optimal power output of generating set and capacitor bank of hybrid power system
Optimal power output, and it is considered as after optimizing the result for wanting to obtain；
4)：Selected, intersected and mutation operator；If being unsatisfactory for step 3) in Optimality Criteria, using with fitness into
The probability of direct ratio determines each individual replicate to the quantity in colony of future generation, the method then intersected using single-point and basic
Position variation method carry out computing；
5)：Colony in the object function for bringing hybrid power RTG EMSs after generation evolution into again by calculating suitable
Angle value is answered, step 3 is then back to) proceed.
The present invention has the effect that and advantage：
1. hybrid power RTG systems of the invention are directed in the case where system energy consumption and non-renewable energy situation is considered, are surpassed
When level electric capacity RTG system entirety energy consumptions are minimum, the optimal power output of generating set and capacitor at each moment of correspondence can be drawn
The optimal power output of group；
2. super capacitor RTG systems of the invention, because primary condition is unknown, are genetic algorithms.System into
This function parameter can also be adjusted according to practical engineering project, if while known primary condition can also apply other optimized algorithm
To solve.
3. the system condition that the present invention considers is more sufficient, has both considered the energy consumption of system generation it is contemplated that non-renewable energy
The factor of amount, is more conform with and puts into practice production application.
Brief description of the drawings
Fig. 1 is hybrid power RTG system construction drawings of the present invention；
Fig. 2 is hybrid power RTG system powers composition distribution map of the present invention；
Fig. 3 is genetic algorithm flow chart of the present invention
Specific embodiment
The present invention in the case where system energy consumption and non-renewable energy is considered, to overall hybrid power system energy pipe
Reason is optimized, and obtains the optimal power output of the diesel generating set and ultracapacitor group under system entirety energy consumption minimum.
A kind of super capacitor RTG energy management methods based on genetic algorithm are proposed to this.According to designed hybrid power system
Structured flowchart, it is proposed that under consideration hybrid power system energy consumption and non-renewable capacity factor, by diesel generating set, super electricity
The characterisitic parameter of container group and loading demand sets up the Mathematical Modeling of hybrid power RTG systems, provides object function and constraint bar
Part, last genetic Optimization Algorithm is solved to function, draws the optimal power output of generating set and capacitor bank.
Initially set up the Mathematical Modeling of hybrid power RTG systems.The mathematical modulo of diesel generating set and ultracapacitor group
Type can be derived from by physical equation.The Mathematical Modeling combination hybrid power RTG system powers composition distribution of non-renewable energy
Figure, can be derived from.Loading demand is distributed in the complete handling of heavy goods flow feelings of known super capacitance hybrid power RTG mono-
RTG binding characteristics parameter can respectively calculate the loading demand of each operation time period under condition.
The setting of object function is in order that the hybrid power energy system of the super capacitor RTG once complete operation cycle
Energy consumption minimumization.These costs mainly include diesel generating set energy, ultracapacitor group energy and non-renewable energy ezpenditure
Cost.
Because the primary condition of object function is unknown, therefore genetic algorithm can be used to be solved.The method is by the U.S.
The Holland of Michigan universities teaches and was proposed in 1969, later again through De Jong, Goldberg et al. induction and conclusion institute
The analoglike evolution algorithm for being formed, its essence is a kind of efficient, parallel, method of global search, and it can be in search procedure
Automatically obtain and accumulate the knowledge about search space, and adaptively command deployment process in the hope of optimal solution.
Hybrid power RTG system architecture diagrams of the present invention, as shown in Figure 1.Hybrid power RTG systems of the invention include bavin
Fry dried food ingredients group of motors, ultracapacitor group and load motor.Wherein, the lifting mechanism of hybrid power RTG is reduced to load motor M,
Diesel generating set and rectifier are connected through gives load motor energy supply to DC (direct current) busbars, ultracapacitor group with it is two-way
The connection of DC/DC converters is parallel to DC busbars again, and when lifting mechanism driving load rises, ultracapacitor group is used for providing peak
Value power, when lifting mechanism declines, ultracapacitor group absorption and regeneration power energy storage.With reference to hybrid power RTG system powers
Composition distribution map, as shown in Fig. 2 non-renewable energy and generating set power, capacitor bank power and load can be derived
The relation of power.Power distribution composition includes diesel generating set power output, the power output of ultracapacitor group and load
Demand power, circle represents hybrid power RTG system capacity relation nodes, and arrow represents the flow direction of energy, generating set output
, used as the overall input energy of system, bearing power is then output energy for both power and capacitor bank power output.Because non-
Regenerated energy volume production is born in the energy difference that system is integrally input into and exports, i.e. generating set power output and capacitor bank power output
Both overall differences with bearing power, so drawing non-renewable energy and generating set power, capacitor bank power and bearing
Carry (3) formula of the relation such as step 3 of power.
Super capacitor RTG energy management method of the present invention based on genetic algorithm, comprises the following steps：
Step one, initially set up the Mathematical Modeling on diesel generating set.Disappeared according to typical diesel-driven generator fuel
The fuel consumption that consumption curve map can obtain diesel-driven generator is normally approximately the quadratic function related to generator power.
Wi f=ai(Pi E)2+biPi E+ci (1)
Wherein, Pi EIt is the power output of generator, is equal to Pi E(t), Pi ET () is that i-th generator is produced in t
Raw power, and meetai、biAnd ciIt is constant, its value is determined by other specifications such as types.Therefore, mix
Diesel generating set form of the fuel consumption that motor is produced in [0, T Δ t] time range is in closing dynamical system：
Wherein, EICEIt is the energy of generating set fuel consumption, Pi EratedIt is the rated output power of engine, T is mixing
One complete cycle of operation of power RTG systems, H is diesel oil calorific value.
Step 2, Mathematical Modeling of the foundation on ultracapacitor group.According to hybrid power RTG systems be in feed, again
When raw braking and standby these three states, corresponding ultracapacitor group be respectively necessary in hybrid power RTG operating process to
Load provides energy, absorption and regeneration energy stores and cluster engine and gives capacitor bank charging energy supply, obtains
Wherein, ESCIt is the energy that capacitor bank is produced, PCT power that () provides for ultracapacitor group in t is while be also
System returns to the power that the power or ultracapacitor group of ultracapacitor group are provided to load, can just can bear.Ought be
When system is in feed condition, i.e., capacitor bank gives system energy supply, now PC(t) ＜ 0；When system is in regenerative braking state or treats
During machine state, i.e., system charges to capacitor bank, now PC(t) ＞ 0.
Step 3, after the Mathematical Modeling for setting up diesel generating set and ultracapacitor group, it is necessary to according to hybrid power
The relation of system capacity sets up the Mathematical Modeling of non-renewable energy.According in actual mechanical process, the energy that capacitor bank absorbs
It is limited, because total system energy consumption, non-renewable energy can still be produced.In order to reduce the generation of non-renewable energy, it is considered to
Difference between generating set and the overall energy supply of capacitor bank and loading demand, the non-renewable cost of energy function for drawing is such as
Under：
Wherein, ENon-reIt is non-renewable energy, PLT () is the demand power in t load, in the present invention load point
Cloth is known.
Step 4, object function is listed according to goal of the invention：
J=EICE+λ×ESC+γ×ENon-re (5)
Wherein, λ and γ are constant-weights, represent overall energy consumption in ultracapacitor group and the ratio of non-renewable energy
Distribution.
Step 5, according to the object function listed, limit the constraints of each variable, including generating set constraint, super
Level capacitor bank must be constrained and constrained with load power demand：
1) constraint of generating set：In order that diesel-driven generator keeps a relatively low specific fuel consumption, it is by operation model
It is have a range of to enclose, and its power limit is Pi Emin≤Pi E(k)≤Pi Emax, wherein Pi EminAnd Pi EmaxRespectively i-th hair
The minimum and maximum power of motivation output.
2) constraint of ultracapacitor group：According to the ultracapacitor operation manual that factory provides, in order to maintain super electricity
The life-span of container, therefore admissible discharge and recharge rate scope is-PChmax≤PC(k)≤PDChmax, wherein PChmaxAnd PDChmaxRespectively
It is allowed maximum charge and discharge power.
3) load power demand constraint：Ensure that hybrid power RTG systems can meet the power demand of load, therefore PE
(k)+PC(k)≥Pd(k)。
Step 6, after object function and constraints is given, with target of the genetic algorithm for solving on cost
Function, so as to obtain the diesel generating set of super capacitance hybrid power RTG and the optimal power output of ultracapacitor group；Tool
Body step is given below referring to Fig. 3：
1)：Individual UVR exposure, produces initial population；Super capacitance hybrid power RTG systems a complete cycle it is negative
Load demand is up-sampled, and sampling number n tries one's best and obeys loading demand curve and be uniformly distributed, by the corresponding load need of n sampled point
Evaluation brings the object function of step 4 into, obtains the target letter of the n variable on generating set power and capacitor bank power
Number, while the constraints according to step 5 carries out gene code to this n variable using unsigned binary number, coding is conciliate
Coded program is mutually changed, and the size value of population size is m, and m values between 40-100, i.e. colony are made up of m individuality, often
It is individual to be produced by random device；
2)：Calculate fitness；According to step 4, on hybrid power RTG energy ezpenditure object function value non-negative, and
It in the hope of functional minimum value is optimization aim to be, therefore directly by the use of target function value as the fitness of the individuality of population m；
3)：Judge whether to meet Optimality Criteria；It is non-when the fitness that hybrid power RTG object functions calculate is brought into
Negative is considered as optimized individual, and the result for drawing is the optimal power output of generating set and capacitor bank of hybrid power system
Optimal power output, and it is considered as after optimizing the result for wanting to obtain；
4)：Selected, intersected and mutation operator；If being unsatisfactory for step 3) in Optimality Criteria, using with fitness into
The probability of direct ratio determines each individual replicate to the quantity in colony of future generation, the method then intersected using single-point and basic
Position variation method carry out computing；
5)：Colony in the object function for bringing hybrid power RTG EMSs after generation evolution into again by calculating suitable
Angle value is answered, step 3 is then back to) proceed.
Claims (1)
1. a kind of super capacitor RTG energy management methods based on genetic algorithm, are applied to hybrid power RTG systems, described mixed
Closing power RTG systems includes diesel generating set, ultracapacitor group and load motor；Load motor is hybrid power energy system
The lifting mechanism of system；Diesel generating set and rectifier connect through DC busbars and give load motor energy supply, ultracapacitor group with
Two-way DC/DC converters connection is parallel to DC busbars again, and when lifting mechanism driving load rises, ultracapacitor group is used for carrying
For peak power, when lifting mechanism declines, ultracapacitor group absorption and regeneration power energy storage；It is characterized in that described based on something lost
The super capacitor RTG energy management methods of propagation algorithm are comprised the following steps：
Step one, the fuel consumption for obtaining diesel generating set according to typical diesel-driven generator fuel consumption curve figure are approximately
The quadratic function related to generator power：
Wi f=ai(Pi E)2+biPi E+ci (1)
Wherein, Pi EIt is the power output of generator, is equal to Pi E(t), Pi ET () is that i-th generator is produced in t
Power, and meetai、biAnd ciIt is constant；Diesel generating set is at [0, T Δ t] in hybrid power system
The form of the fuel consumption that load motor is produced is in time range：
Wherein, EICEIt is the energy of generating set fuel consumption, Pi EratedIt is the rated output power of engine, T is hybrid power
One complete cycle of operation of system, H is diesel oil calorific value；
Step 2, feed, regenerative braking are according to hybrid power RTG systems and during standby these three states, it is corresponding super
Capacitor bank is respectively necessary for being deposited to load motor offer energy, absorption and regeneration energy in hybrid power RTG system operation procedures
Storage and cluster engine give capacitor bank charging energy supply, obtain
Wherein, ESCIt is the energy that ultracapacitor group is produced, PCT power that () is provided in t for ultracapacitor group is simultaneously
It is also that system returns to the power that the power or ultracapacitor group of ultracapacitor group are provided to load, can just can bears；I.e.
When system is in feed condition, i.e. ultracapacitor group to system energy supply, PC(t) ＜ 0；When system is in regenerative braking state
Or holding state, i.e. system to ultracapacitor group when charging, PC(t) ＞ 0；
Step 3, non-renewable cost of energy function are as follows：
Wherein, ENon-reIt is non-renewable energy, PLT () is that load distribution is known in the demand power of t load；
Step 4, list object function：
J=EICE+λ×ESC+γ×ENon-re (5)
Wherein, λ and γ are constant-weights, represent ratio point of the overall energy consumption in ultracapacitor group and non-renewable energy
Match somebody with somebody；
Step 5, constraint, super electricity according to the object function listed, the constraints of each variable of restriction, including generating set
Container group must be constrained and constrained with load power demand：
1) constraint of generating set：The power limit of diesel generating set is Pi Emin≤Pi E(k)≤Pi Emax, wherein Pi EminWith
Pi EmaxThe respectively i-th minimum and maximum power of engine output；
2) constraint of ultracapacitor group：Admissible discharge and recharge rate scope is-PChmax≤PC(k)≤PDChmax, wherein PChmaxWith
PDChmaxIt is allowed maximum charge and discharge power respectively；
3) load power demand constraint：PE(k)+PC(k)≥Pd(k)；
Step 6, after object function and constraints is given, with object function of the genetic algorithm for solving on cost,
So as to obtain the diesel generating set of super capacitance hybrid power RTG and the optimal power output of ultracapacitor group；Specific step
It is rapid as follows：
1)：Individual UVR exposure, produces initial population；Needed in the load of a complete cycle of super capacitance hybrid power RTG systems
Up-sampling is sought, sampling number n tries one's best and obeys being uniformly distributed for loading demand curve, by the corresponding loading demand value of n sampled point
Bring the object function of step 4 into, obtain the object function of the n variable on generating set power and capacitor bank power, together
When gene code is carried out to this n variable using unsigned binary number according to the constraints of step 5, code and decode journey
Sequence is mutually changed, and the size value of population size is m, and m values between 40-100, i.e. colony are made up of m individuality, per each and every one
Body is produced by random device；
2)：Calculate fitness；According to step 4, on hybrid power RTG energy ezpenditure object function value non-negative, and be with
The minimum value for finding a function for optimization aim, therefore directly by the use of target function value as population m fitness of individuality；
3)：Judge whether to meet Optimality Criteria；When bringing fitness that hybrid power RTG object functions calculate into for non-negative i.e.
It is considered as optimized individual, the result for drawing is optimal for the optimal power output of generating set and capacitor bank of hybrid power system
Power output, and it is considered as after optimizing the result for wanting to obtain；
4)：Selected, intersected and mutation operator；If being unsatisfactory for step 3) in Optimality Criteria, be directly proportional using to fitness
Probability determine each individual replicate to the quantity in colony of future generation, the method then intersected using single-point and basic position
Variation method carries out computing；
5)：Colony in the object function for bringing hybrid power RTG EMSs into again after generation evolution by calculating fitness
Value, is then back to step 3) proceed.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108494080A (en) * | 2018-03-16 | 2018-09-04 | 上海海事大学 | A kind of hybrid power ship multiple target energy optimizing method based on improvement NSGA-II |
| CN108988414A (en) * | 2018-06-28 | 2018-12-11 | 南京理工大学 | Bidirectional DC-DC converter control system and method based on hybrid power RTG |
| CN110417107A (en) * | 2019-07-29 | 2019-11-05 | 上海海事大学 | The Energy Management System and management method of photovoltaic hybrid power gantry |
| CN110834624A (en) * | 2019-11-11 | 2020-02-25 | 常熟理工学院 | Full hybrid vehicle energy efficiency optimization control method based on adaptive genetic algorithm |
| CN113659602A (en) * | 2021-10-19 | 2021-11-16 | 深圳市今朝时代股份有限公司 | Electric energy management system and method based on super capacitor |
| CN113910925A (en) * | 2021-10-25 | 2022-01-11 | 浙江大学杭州国际科创中心 | ECMS-based super capacitor-lithium battery hybrid RTG energy optimization management strategy |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1504399A (en) * | 2002-12-04 | 2004-06-16 | 上海振华港口机械（集团）股份有限公 | Tire type gantry container crane using super capacitor |
| WO2009001604A1 (en) * | 2007-06-26 | 2008-12-31 | Sumitomo Heavy Industries Engineering & Services Co., Ltd. | Hybrid power supply device and method of controlling hybrid power supply device |
| CN103606714A (en) * | 2013-11-20 | 2014-02-26 | 上海海事大学 | Graded limiting device and method for energy storage and discharge of hybrid RTG (radioisotope thermoelectric generator) power battery |
| CN103902790A (en) * | 2014-04-25 | 2014-07-02 | 上海海事大学 | Dynamic optimization design method of quay crane |
| CN204481536U (en) * | 2015-04-16 | 2015-07-15 | 哈尔滨巨容新能源有限公司 | Utilize super capacitor energy-storage type E-RTG transition power supply |
-
2017
- 2017-04-14 CN CN201710245398.9A patent/CN106911139B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1504399A (en) * | 2002-12-04 | 2004-06-16 | 上海振华港口机械（集团）股份有限公 | Tire type gantry container crane using super capacitor |
| WO2009001604A1 (en) * | 2007-06-26 | 2008-12-31 | Sumitomo Heavy Industries Engineering & Services Co., Ltd. | Hybrid power supply device and method of controlling hybrid power supply device |
| CN103606714A (en) * | 2013-11-20 | 2014-02-26 | 上海海事大学 | Graded limiting device and method for energy storage and discharge of hybrid RTG (radioisotope thermoelectric generator) power battery |
| CN103902790A (en) * | 2014-04-25 | 2014-07-02 | 上海海事大学 | Dynamic optimization design method of quay crane |
| CN204481536U (en) * | 2015-04-16 | 2015-07-15 | 哈尔滨巨容新能源有限公司 | Utilize super capacitor energy-storage type E-RTG transition power supply |
Non-Patent Citations (1)
| Title |
|---|
| 杨磊: "应用超级电容的轮胎式集装箱起重机混合动力系统研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108494080A (en) * | 2018-03-16 | 2018-09-04 | 上海海事大学 | A kind of hybrid power ship multiple target energy optimizing method based on improvement NSGA-II |
| CN108494080B (en) * | 2018-03-16 | 2024-01-30 | 上海海事大学 | Improved NSGA-II-based hybrid power ship multi-objective energy optimization method |
| CN108988414A (en) * | 2018-06-28 | 2018-12-11 | 南京理工大学 | Bidirectional DC-DC converter control system and method based on hybrid power RTG |
| CN110417107A (en) * | 2019-07-29 | 2019-11-05 | 上海海事大学 | The Energy Management System and management method of photovoltaic hybrid power gantry |
| CN110834624A (en) * | 2019-11-11 | 2020-02-25 | 常熟理工学院 | Full hybrid vehicle energy efficiency optimization control method based on adaptive genetic algorithm |
| CN113659602A (en) * | 2021-10-19 | 2021-11-16 | 深圳市今朝时代股份有限公司 | Electric energy management system and method based on super capacitor |
| CN113659602B (en) * | 2021-10-19 | 2022-02-18 | 深圳市今朝时代股份有限公司 | Electric energy management system and method based on super capacitor |
| CN113910925A (en) * | 2021-10-25 | 2022-01-11 | 浙江大学杭州国际科创中心 | ECMS-based super capacitor-lithium battery hybrid RTG energy optimization management strategy |
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