CN107134810A - A kind of micro- energy net energy-storage system of self distributes method for solving rationally - Google Patents
A kind of micro- energy net energy-storage system of self distributes method for solving rationally Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- 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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
-
- 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
-
- H02J3/386—
-
- 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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- 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
- H02J3/002—Flicker reduction, e.g. compensation of flicker introduced by non-linear load
-
- 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Abstract
Method for solving is distributed rationally the invention discloses a kind of micro- energy net energy-storage system of self, and its step includes：Establish the self-sufficient probability demand of micro- energy net；Set up the energy-storage system optimization collocation model of micro- energy net；For energy-storage system optimization collocation model, the life of storage battery is estimated using rain flow method, investment coefficient is drawn；By predicting 1 year load data, wind-powered electricity generation data take appropriate operation reserve during dry run, and choose to go through the energy-storage system that algorithm or particle cluster algorithm seek to optimize according to Search Range and configure.The present invention proposes self-sufficient probability demand while economy is met, and pursues the stability of a system；The stability of a system is maintained while reaching economic optimum, equalization point is found between investment cost, operating cost, pollution treatment expense three.
Description
Technical field
The present invention relates to the micro- energy net energy-storage system planning technology field of self, and in particular to independent micro- energy net electricity storage
It can match somebody with somebody with optimization collocation method for solving of both hot energy storage, more particularly to a kind of micro- energy net energy-storage system optimization of self
Put method for solving.
Background technology
With the increasingly exhausted and environmental problem of traditional fossil energy, global warming problem it is increasingly serious, energetically
Develop using wind, light as the low-carbon new energy of representative, improving the regenerative resource permeability of existing power network turns into solution problem above
One of important channel.Thus, the energy internet concept that Jeremy's Jeremy Rifkin is proposed has triggered extensive concern.Wherein,
Micro- energy net in the region such as factory, large buildings, city and rural area centralized residential district, isolated island is can be applied to, it is used as energy
The important composition form of source internet, will be one of trend of future source of energy System Development.The concept of micro- energy net is in micro- electricity
Net is conceptive to be developed, and is generally comprised hot and cold, the gentle 4 kinds of energy forms of electricity, is utilized technology of Internet of things and information technology pair
All powering device unified integrations in region simultaneously implement scheduling, optimize energy supply to the cold and hot electric load in region to reach, carry
Rise the efficiency of using energy source.
But, the regenerative resource using scene as representative has very strong intermittence and stochastic volatility again, frequently can lead to
Abandon the generation that wind abandons the phenomenons such as light.Especially during heat supply, the operational mode of cogeneration units " electricity determining by heat " can be reduced
The peak modulation capacity of whole micro- energy net electric energy, or even cause substantial amounts of " abandoning wind ".For regenerative resource of dissolving, strengthen micro- energy
The flexibility of net, we introduce the energy-storage system stored available for multipotency in micro- energy net.Independence involved in the present invention
The micro- energy net energy-storage system of type includes two kinds of forms of storing up electricity and heat accumulation.
In summary, within existing micro- energy screen frame frame, it is necessary to invent a kind of optimal configuration for energy-storage system
Method, to reach whole system under certain stability, realize economy it is optimal, while drop damage purpose.
The content of the invention
Distribute method for solving rationally it is an object of the invention to provide a kind of micro- energy net energy-storage system of self, make its
Economic optimum is reached in independent micro- energy net, while the stability of the system of maintenance.
Energy-storage system is divided into electric energy-storage system (battery) and hot energy-storage system, and the configuration of energy-storage system is matched somebody with somebody including power
Put and capacity configuration.
Wherein, energy-storage system of accumulator includes battery, the equipment such as current transformer, so cost of investment is respectively with power and appearance
Two kinds of form clearing of amount.Heat reservoir includes heat storage can and Heat Conduction Material etc., so cost of investment is same with power and capacity two
The clearing of the form of kind.
In order to solve above-mentioned technical problem, propose that following technical scheme is realized：
It is the investment cost of the configuration influence energy-storage system of energy-storage system, whole micro- in the micro- energy net energy-storage system of self
Three aspects of operating cost, pollution treatment expense of power network；The relatively low energy-storage system of configuration does not reach economy expected from system and steady
It is qualitative, it is impossible to effectively to reduce operating cost, the CO of discharge2It is higher with pernicious gas content；And configure higher energy-storage system and throw
Money is costly, and overall maintenance cost is also of a relatively high；Therefore, a kind of micro- energy net energy-storage system of self, which is distributed rationally, asks
In solution method, the configuration of optimal energy-storage system selection can reach flat between investment cost, operating cost, pollution treatment expense three
Weighing apparatus, finds three's sum at equalization point, i.e., total expense reaches the energy-storage system configuration of minimum.
The step of micro- energy net energy-storage system of a kind of self distributes method for solving rationally is as follows：
Step 1 establishes the self-sufficient probability demand of micro- energy net；
Step 2 sets up the energy-storage system optimization collocation model of micro- energy net；
Step 3 is directed to energy-storage system optimization collocation model, estimates the life of storage battery using rain flow method, draws investment
Coefficient；
Step 4 is by predicting 1 year load data, and wind-powered electricity generation data take appropriate operation plan during dry run
Slightly, and algorithm is chosen to go through according to Search Range or particle cluster algorithm seeks the energy-storage system configuration of optimization.
Further, in step 1, the self-sufficient probability demand, its model is：
Wherein, PSSeAnd PSShElectric load and the self-sufficient probability of thermic load in micro- energy net respectively, Δ w, Δ d and
The wind that Δ h meets normal distribution respectively is exerted oneself predicated error.
Further, in step 2, the energy-storage system optimization collocation model for setting up micro- energy net, its specific mistake
Journey includes following content：
The synthesis totle drilling cost of consideration investment, operation and pollution treatment environment and fuel cost is needed in a model, with totle drilling cost
Minimum object function, sets up model：
Min(IC+OC+PC) (3)
In formula, IC is energy-storage system investment cost, and OC is micro- energy network operation expense, and PC is micro- energy net pollution treatment cost, α
It is the unit power investment coefficient of battery, β is the unit capacity investment coefficient of battery, and χ is the unit power of heat reservoir
Investment coefficient, δ is the unit capacity investment coefficient of heat reservoir,It is the peak power of battery,Be battery most
Large Copacity,It is the peak power of heat reservoir,It is the maximum capacity of heat reservoir, NT is total number of days, and NH is total
Hourage, NG is total conventional fired power generating unit number, and NL is total cogeneration units number, PithIt is certain conventional power unit in certain period
The power of interior generation, FeIt is the functional relation of the power and expense, IithIt is the state indices whether distributed power source works, work
As 1, do not work as 0, PlthIt is the power that certain cogeneration units is produced within certain period, FhIt is the power and expense
Functional relation, LlthIt is the state indices whether distributed power source works, works as 1, do not work as 0；SUth, SDthRespectively
The start and stop expense of generating set, αKRepresent the control expense coefficient of different pollutants, βKRepresent the discharge system of different pollutants
Number, NK represents the total amount of pollutant；
Wherein, the unit capacity investment coefficient of battery is：
In formula, CEIt is total electric energy storage unit capacity cost of investment, CmIt is maintenance, the maintenance cost of equipment per unit capacity
With device disposal costs sum；In the cycle lie that these expenses are divided to energy-storage system, so as to obtain in planning horizon
Specific investment cost coefficient；
Coal consumption cost can be expressed as the quadratic function form of generated output；Conventional fired power generating unit and cogeneration units
Power cost function is respectively：
The constraints of the energy-storage system optimization collocation model of micro- energy net is as follows：
Electrical power Constraints of Equilibrium, heat supply Constraints of Equilibrium, wind power output constraint, Unit commitment, power storage system constraint and heat accumulation
System restriction；Bound that the Unit commitment is constrained including unit output bound, steam-extracting type unit heat is exerted oneself is constrained, steam-extracting type
The hot Climing constant of the net generated output bound constraint of unit, the general power Climing constant of unit and steam-extracting type unit；It is described about
The particular content of beam condition is：
(1) electrical power Constraints of Equilibrium：
In formula, NR is new energy quantity, PrthIt is the power that new energy is produced, PESSIt is energy-storage system charge or discharge
Power, is charged as negative value, electric discharge be on the occasion of；Pload,thThe power for needed for the period load；
(2) heat supply Constraints of Equilibrium：
In formula, hlthFor thermoelectricity unit i the period thermal power；hhsFor the storage of the period heat storage can, heat release power；Put
Heat is on the occasion of heat accumulation is negative value, hthFor the thermic load of the system period；NL is the number of units of all thermoelectricity units；
(3) wind power output is constrained：
In formula,It is the rated power of wind-driven generator, vCI, vRAnd vCOThe incision wind speed of blower fan is represented respectively, it is specified
Wind speed and cut-out wind speed, vhtIt is the wind speed of certain period；
(4) Unit commitment：
1) unit output bound is constrained：
pi,min≤pi,t≤pi,max (13)
In formula, pi,min、pi,maxRespectively minimum of the unit under pure condensate operating mode, EIAJ；
2) thermoelectricity unit heat exert oneself upper and lower limit constraint：
0≤hi,t≤hi,max (14)
In formula, hi,maxThe threshold limit value exerted oneself for unit i heat, the value depends primarily on the size of capacity of heat exchanger；
3) the general power Climing constant of unit：
Pith-Pit(h-1)≤URi(1-yith)+Pi minyith (15)
Pith(h-1)-Pith≤DRi(1-zith)+Pi minzith (16)
In formula, URiIt is to ramp up limitation, yithBe the unit whether starting state amount, Pi minBe the generating set most
Small generated energy, DRiIt is ramp down limitation, zithBe the unit whether stopped status amount；
4) the hot Climing constant of thermoelectricity unit：
hi,t-hi,t-1≤Δhu,i (17)
hi,t-1-hi,t≤Δhd,i (18)
In formula, Δ hu,i、Δhd,iThe maximum variable quantity of thermal power respectively in the steam-extracting type unit unit interval；
(5) power storage system is constrained：
Charging process is
Discharge process is
SOC (t) is the dump energy of energy-storage system at the end of t-th period；At the end of SOC (t-1) is the t-1 period
The dump energy of energy-storage system；δ is the self-discharge rate of energy-storage system；Pc、PdThe respectively charge and discharge power of energy-storage system；ηc、
ηdThe respectively charge and discharge efficiency of power storage system；Ce maxFor the rated capacity of power storage system；
(6) heat reservoir is constrained：
In formula, HHS(t) it is the hot stored energy capacitances of period t；μ is hot energy storage radiation loss rate；QHS_ch(t)、QHS_dis(t)With
ηhch、ηhdisRespectively period t suction heat release power and efficiency.
Further, in step 3, the use rain flow method estimation life of storage battery, its detailed process is as follows：
The life of storage battery is estimated using rain flow method, rain flow method can be described as " tower top method " again, be by Britain
What two engineers of Matsuiski and Endo proposed, rain flow method is mainly used in engineering circles, especially in Calculation of Fatigue Life
With widely.Strain-time history data record is turned over 90 °, straight down, data record is just as one for time coordinate axle
Serial roofing, rainwater is past dirty along roofing, therefore referred to as rain flow method.
The life-span of energy-storage battery is by the depth of discharge including battery, high rate performance, discharge and recharge blanking voltage and environment temperature
The influence of factor；Influence of the high rate performance to its life-span of battery is not considered, and the peak power of energy-storage battery takes rated value；Do not examine
Consider influence of the discharge and recharge blanking voltage of battery to its life-span, set the capacity span of energy-storage battery；Do not consider temperature pair
The influence of battery life, environment temperature is considered as room temperature；By simplification, the life-span of energy-storage battery is estimated using below equation：
T=ceil (1/365.dloss) (23)
Wherein, dlossFor the electric energy-storage system life-span damage rate of one day, θ was periodic coefficient, and the complete period is 1, and the half period is
0.5；CyciFor the corresponding maximum cycle of ith cycle period；T is life cycle, and ceil is flow in upper plenum；Pass through structure
Build the charging and discharging curve of power storage system in one day, it becomes possible to estimate the life-span of energy-storage system.
Further, in step 4, it is described algorithm to be chosen to go through according to Search Range or particle cluster algorithm seeks optimal
The energy-storage system configuration of change, its detailed process is as follows：
According to energy-storage system configure scope come determine use method, if scope is smaller, configuration categories more at least using time
Go through algorithm；If scope is larger, the energy-storage system configuration for seeking to optimize using particle cluster algorithm draws power storage system respectively
Capacity, the capacity of power and heat reservoir, power；When Search Range is smaller, the energy storage of self-sufficient Probability Condition is being met
In system configuration scope, choose a kind of configuration and calculate cost, and judge whether to complete it is used in given range configure, if complete to
The traversal for determining all configurations in scope then completes calculating process.Do not complete to travel through a kind of calculating of configuration under then continuation；It is such a
Method is more comprehensive, it is adaptable to the less optimization problem of scope；
When Search Range is larger, particle swarm optimization algorithm is taken, the algorithm is a kind of optimization tool based on iteration；Should
Algorithm starts from one group of RANDOM SOLUTION, and optimal solution is searched for by continuous iteration；In each iteration, by track individual extreme value and
Global extremum carrys out Population Regeneration；Because the algorithm has the advantages that to realize that simple, Fast Convergent and precision are higher, have been widely used
In engineering practice.
The basic step of particle cluster algorithm is as follows：
1) position of each particulate and speed in random initializtion population；
2) fitness of each particulate is evaluated, the position of each current particulate and adaptive value are stored in each particulate
In pbest, the position of adaptive value optimum individual and adaptive value in all pbest are stored in gbest；
3) formula (17) h is usedi,t-hi,t-1≤Δhu,iThe speed of more new particle and displacement：
4) to each particulate, its adaptive value is made comparisons with the desired positions that it is lived through, if preferably, as
Current desired positions；
5) relatively more current all pbest and gbest value, update gbest；
If 6) meet the stop condition of default operational precision or iterations, search stops, and otherwise output result returns
Step 3, continue search for.
The present invention has the beneficial effect that compared with prior art：
1st, Consideration of the present invention comprehensively, is proposed under energy Background of Internet, energy LAN multipotency storage system is matched somebody with somebody
The method put；Consider the life-span of electric energy-storage system, application background updates；
2nd, the present invention proposes self-sufficient probability demand while economy is met, and pursues the stability of a system；Reach through
The stability of a system is maintained while helping optimal, equalization point is found between investment cost, operating cost, pollution treatment expense three；
3rd, the present invention is on algorithms selection, and according to the specifically chosen one of which algorithm of Search Range, result of calculation is more smart
Really；
4th, the present invention more conforms to the characteristic of independent micro- energy net, practical, and economy is reached in independent micro- energy net
It is optimal, while the stability of the system of maintenance；Certain stability be issued to whole system realize economy it is optimal, while drop damage
Purpose.
Brief description of the drawings
Fig. 1 is the general principle figure of the inventive method；
Fig. 2 is the basic flow sheet of the present invention；
Fig. 3 is the explanation figure of rain flow method.
Embodiment
The present invention will be further described below in conjunction with the accompanying drawings.
A kind of micro- energy net energy-storage system of self of the present invention distributes method for solving rationally, its general principle figure such as Fig. 1
It is shown, three sides of investment cost, the operating cost of whole micro-capacitance sensor, pollution treatment expense of the configuration influence energy-storage system of energy-storage system
Face；The relatively low energy-storage system of configuration does not reach economy expected from system and stability, it is impossible to effectively reduce operating cost, discharges
CO2It is higher with some pernicious gas contents；And configure higher, the overall maintenance cost of higher energy-storage system investment cost
It is of a relatively high；Therefore, the configuration selection of optimal energy-storage system can reach between investment cost, operating cost, pollution treatment expense three
To balance, three's sum is found at equalization point, namely total expense reaches the energy-storage system configuration of minimum；
The basic flow sheet of the inventive method is as shown in Fig. 2 as follows the step of methods described：
Step 1, the self-sufficient probability demand of micro- energy net is established, self-sufficient probability demand model is set up；It is described from
It is to self-sustaining probability demand model：
Wherein, PSSeAnd PSShElectric load and the self-sufficient probability of thermic load in micro- energy net respectively, Δ w, Δ d and
The wind that Δ h meets normal distribution respectively is exerted oneself predicated error.
Step 2, the energy-storage system optimization collocation model of micro- energy net is set up；
The energy-storage system optimization collocation model for setting up micro- energy net, its detailed process includes following content：
The synthesis totle drilling cost of consideration investment, operation and pollution treatment environment and fuel cost is needed in a model, with totle drilling cost
Minimum object function, setting up model is：
Min(IC+OC+PC) (3)
In formula, IC is energy-storage system investment cost, and OC is microgrid operating cost, and PC is micro- energy net pollution treatment cost, and α is to store
The unit power investment coefficient of battery, β is the unit capacity investment coefficient of battery, and χ is the unit power investment of heat reservoir
Coefficient, δ is the unit capacity investment coefficient of heat reservoir,It is the peak power of battery,It is the maximum appearance of battery
Amount,It is the peak power of heat reservoir,It is the maximum capacity of heat reservoir, NT is total number of days, and NH is total hour
Number, NG is total conventional fired power generating unit number, and NL is total cogeneration units number, PithIt is that certain conventional power unit is produced within certain period
Raw power, FeIt is the functional relation of the power and expense, IithIt is the state indices whether distributed power source works, working is
1, do not work as 0, PlthIt is the power that certain cogeneration units is produced within certain period, FhIt is the power and the function of expense
Relation, LlthIt is the state indices whether distributed power source works, works as 1, do not work as 0；SUth, SDthRespectively generate electricity
The start and stop expense of unit, αKRepresent the control expense coefficient of different pollutants, βKRepresent the emission factor of different pollutants, NK
Represent the total amount of pollutant；
Wherein it is for the unit capacity investment coefficient of battery：
In formula, CEIt is total electric energy storage unit capacity cost of investment, CmIt is maintenance, the maintenance cost of equipment per unit capacity
With device disposal costs sum；In the cycle lie that these expenses are divided to energy-storage system, so as to obtain in planning horizon
Specific investment cost coefficient；
Coal consumption cost can be expressed as the quadratic function form of generated output；Conventional fired power generating unit and cogeneration units
Power cost function is respectively：
The constraints of the energy-storage system optimization collocation model of micro- energy net is as follows：
Electrical power Constraints of Equilibrium, heat supply Constraints of Equilibrium, wind power output constraint, Unit commitment, power storage system constraint and heat accumulation
System restriction；Bound that the Unit commitment is constrained including unit output bound, steam-extracting type unit heat is exerted oneself is constrained, steam-extracting type
The hot Climing constant of the net generated output bound constraint of unit, the general power Climing constant of unit and steam-extracting type unit；It is described about
The particular content of beam condition is：
(1) electrical power Constraints of Equilibrium：
In formula, NR is new energy quantity, PrthIt is the power that new energy is produced, PESSIt is energy-storage system charge or discharge
Power, is charged as negative value, electric discharge be on the occasion of；Pload,thThe power for needed for the period load；
(2) heat supply Constraints of Equilibrium：
In formula, hlthFor thermoelectricity unit i the period thermal power；hhsFor the storage of the period heat storage can, heat release power；Put
Heat is on the occasion of heat accumulation is negative value, hthFor the thermic load of the system period；NL is the number of units of all thermoelectricity units；
(3) wind power output is constrained：
In formula,It is the rated power of wind-driven generator, vCI, vRAnd vCOThe incision wind speed of blower fan is represented respectively, it is specified
Wind speed and cut-out wind speed, vhtIt is the wind speed of certain period；
(4) Unit commitment：
1) unit output bound is constrained：
pi,min≤pi,t≤pi,max (13)
In formula, pi,min、pi,maxRespectively minimum of the unit under pure condensate operating mode, EIAJ；
2) thermoelectricity unit heat exert oneself upper and lower limit constraint：
0≤hi,t≤hi,max (14)
In formula, hi,maxThe threshold limit value exerted oneself for unit i heat, the value depends primarily on the size of capacity of heat exchanger；
3) the general power Climing constant of unit：
Pith-Pit(h-1)≤URi(1-yith)+Pi minyith (15)
Pith(h-1)-Pith≤DRi(1-zith)+Pi minzith (16)
In formula, URiIt is to ramp up limitation, yithBe the unit whether starting state amount, Pi minBe the generating set most
Small generated energy, DRiIt is ramp down limitation, zithBe the unit whether stopped status amount；
4) the hot Climing constant of thermoelectricity unit：
hi,t-hi,t-1≤Δhu,i (17)
hi,t-1-hi,t≤Δhd,i (18)
In formula, Δ hu,i、Δhd,iThe maximum variable quantity of thermal power respectively in the steam-extracting type unit unit interval；
(5) power storage system is constrained：
Charging process is
Discharge process is
SOC (t) is the dump energy of energy-storage system at the end of t-th period；At the end of SOC (t-1) is the t-1 period
The dump energy of energy-storage system；δ is the self-discharge rate of energy-storage system；Pc、PdThe respectively charge and discharge power of energy-storage system；ηc、
ηdThe respectively charge and discharge efficiency of power storage system；For the rated capacity of power storage system；
(6) heat reservoir is constrained：
In formula, HHS(t) it is the hot stored energy capacitances of period t；μ is hot energy storage radiation loss rate；QHS_ch(t)、QHS_dis(t)With
ηhch、ηhdisRespectively period t suction heat release power and efficiency.
Step 3, for energy-storage system optimization collocation model, the life of storage battery is estimated using rain flow method, throwing is drawn
Provide coefficient；
The use rain flow method estimates the life of storage battery, and its detailed process is as follows：
The life of storage battery is estimated using rain flow method, rain flow method can be described as " tower top method " again, be by Britain
What two engineers of Matsuiski and Endo proposed, rain flow method is mainly used in engineering circles, especially in Calculation of Fatigue Life
With widely.Strain-time history data record is turned over 90 °, straight down, data record is just as one for time coordinate axle
Serial roofing, rainwater is past dirty along roofing, therefore referred to as rain flow method.
The life-span of energy-storage battery is by the depth of discharge including battery, high rate performance, discharge and recharge blanking voltage and environment temperature
The influence of factor；Influence of the high rate performance to its life-span of battery is not considered, and the peak power of energy-storage battery takes rated value；Do not examine
Consider influence of the discharge and recharge blanking voltage of battery to its life-span, set the capacity span of energy-storage battery；Do not consider temperature pair
The influence of battery life, environment temperature is considered as room temperature；
1) curve of SOC-time is turned over clockwise, rain stream is in the starting point of record and according to this in the inner edge of each peak value
Start；
2) (i.e. eaves) are lower vertically to be dripped rain stream flowing at peak value, and maximum is flowed to when opposite has one than starting always more
Untill positive maximum, or, flow to always when opposite has one than beginning untill the more negative minimum value of minimum value；
3) when rain stream runs into the rain flowed down from roof above, flowing is just stopped, and constitute a circulation；
4) beginning and end flowed according to raindrop, draws each circulation, all circulations is taken out one by one, and record it
Peak-to-valley value；
5) horizontal length of each rain stream as the circulation depth of discharge.
As shown in figure 3, grey heavy line (A-B-C-D-E-F-G) represents battery SOC change curve.Utilize rain-flow counting
Method, can obtain cycle count period 1 (B-C-B', depth of discharge be 0.075), the cycle 2, (E-F-E', depth of discharge was 0.05) and to follow
The ring count half period 3 (A-B-B'-D, depth of discharge be 0.28), the half period 4, (0.2) D-E-E'-G, depth of discharge was.
By simplification, the life-span of energy-storage battery is estimated using below equation：
T=ceil (1/365.dloss) (23)
Wherein, dlossFor the electric energy-storage system life-span damage rate of one day, θ was periodic coefficient, and the complete period is 1, and the half period is
0.5；CyciFor the corresponding maximum cycle of ith cycle period；T is life cycle, and ceil is flow in upper plenum；Pass through structure
Build the charging and discharging curve of power storage system in one day, it becomes possible to estimate the life-span of energy-storage system.
Step 4, by predicting 1 year load data, wind-powered electricity generation data take appropriate operation plan during dry run
Slightly, and algorithm is chosen to go through according to Search Range or particle cluster algorithm seeks the energy-storage system configuration of optimization.
It is described algorithm to be chosen to go through according to Search Range or energy-storage system that particle cluster algorithm is sought to optimize is configured, its
Detailed process is as follows：Configure scope according to energy-storage system to determine the method used, if scope is smaller, configuration categories are adopted more at least
Use ergodic algorithm；If scope is larger, the energy-storage system configuration for seeking to optimize using particle cluster algorithm draws storing up electricity system respectively
Capacity, the capacity of power and heat reservoir, the power of system；When Search Range is smaller, self-sufficient Probability Condition is being met
In energy-storage system configuration scope, one kind configuration is chosen, cost is calculated and judges whether to complete configuration used in given range, if complete
The traversal of all configurations then completes calculating process in into given range.Do not complete to travel through a kind of calculating of configuration under then continuation；
Such a method is more comprehensive, it is adaptable to the less optimization problem of scope；
When Search Range is larger, particle swarm optimization algorithm is taken, the algorithm is a kind of optimization tool based on iteration；Should
Algorithm starts from one group of RANDOM SOLUTION, and optimal solution is searched for by continuous iteration；In each iteration, by track individual extreme value and
Global extremum carrys out Population Regeneration；Because the algorithm has the advantages that to realize that simple, Fast Convergent and precision are higher, have been widely used
In engineering practice.
The basic step of particle cluster algorithm is as follows：
1) position of each particulate and speed in random initializtion population；
2) fitness of each particulate is evaluated, the position of each current particulate and adaptive value are stored in each particulate
In pbest, the position of adaptive value optimum individual and adaptive value in all pbest are stored in gbest；
3) formula (17) h is usedi,t-hi,t-1≤Δhu,iThe speed of more new particle and displacement：
4) to each particulate, its adaptive value is made comparisons with the desired positions that it is lived through, if preferably, as
Current desired positions；
5) relatively more current all pbest and gbest value, update gbest；
If 6) meet the stop condition of default operational precision or iterations, search stops, and otherwise output result returns
Step 3, continue search for.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention
Enclose and be defined, on the premise of design spirit of the present invention is not departed from, technical side of the those of ordinary skill in the art to the present invention
In various modifications and improvement that case is made, the protection domain that claims of the present invention determination all should be fallen into.
Claims (5)
1. a kind of micro- energy net energy-storage system of self distributes method for solving rationally, it is characterised in that：
Three sides of investment cost, the operating cost of whole micro-capacitance sensor, pollution treatment expense of the configuration influence energy-storage system of energy-storage system
Face；The relatively low energy-storage system of configuration does not reach economy expected from system and stability, it is impossible to effectively reduce operating cost, discharges
CO2It is higher with pernicious gas content；And the higher energy-storage system investment cost of configuration is higher, overall maintenance cost is also relative
It is higher；Therefore, the configuration selection of optimal energy-storage system can reach flat between investment cost, operating cost, pollution treatment expense three
Weighing apparatus, finds three's sum at equalization point, i.e., total expense reaches the energy-storage system configuration of minimum；
The step of the method for the invention, is as follows：
Step 1 establishes the self-sufficient probability demand of micro- energy net；
Step 2 sets up the energy-storage system optimization collocation model of micro- energy net；
Step 3 is directed to energy-storage system optimization collocation model, estimates the life of storage battery using rain flow method, draws investment department
Number；
Step 4 is by predicting 1 year load data, and wind-powered electricity generation data take appropriate operation reserve during dry run, and
Algorithm is chosen to go through according to Search Range or particle cluster algorithm seeks the energy-storage system configuration of optimization.
2. the micro- energy net energy-storage system of a kind of self according to claim 1 distributes method for solving rationally, its feature exists
In：In step 1, the self-sufficient probability demand, its model is：
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Wherein, PSSeAnd PSShIt is h points of electric load and the self-sufficient probability of thermic load in micro- energy net, Δ w, Δ d and Δ respectively
The wind for not meeting normal distribution is exerted oneself predicated error.
3. the micro- energy net energy-storage system of a kind of self according to claim 1 distributes method for solving rationally, its feature exists
In：In step 2, the energy-storage system optimization collocation model for setting up micro- energy net, its detailed process includes following content：
The synthesis totle drilling cost of consideration investment, operation and pollution treatment environment and fuel cost is needed in a model, it is minimum with totle drilling cost
For object function, model is set up：
Min(IC+OC+PC) (3)
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In formula, IC is energy-storage system investment cost, and OC is micro- energy network operation expense, and PC is micro- energy net pollution treatment cost, and α is to store
The unit power investment coefficient of battery, β is the unit capacity investment coefficient of battery, and χ is the unit power investment of heat reservoir
Coefficient, δ is the unit capacity investment coefficient of heat reservoir,It is the peak power of battery,It is the maximum appearance of battery
Amount,It is the peak power of heat reservoir,It is the maximum capacity of heat reservoir, NT is total number of days, and NH is total hour
Number, NG is total conventional fired power generating unit number, and NL is total cogeneration units number, PithIt is that certain conventional power unit is produced within certain period
Raw power, FeIt is the functional relation of the power and expense, IithIt is the state indices whether distributed power source works, working is
1, do not work as 0, PlthIt is the power that certain cogeneration units is produced within certain period, FhIt is the power and the function of expense
Relation, LlthIt is the state indices whether distributed power source works, works as 1, do not work as 0；SUth, SDthRespectively generate electricity
The start and stop expense of unit, αKRepresent the control expense coefficient of different pollutants, βKRepresent the emission factor of different pollutants, NK
Represent the total amount of pollutant；
Wherein, the unit capacity investment coefficient of battery is：
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In formula, CEIt is total electric energy storage unit capacity cost of investment, CmIt is maintenance, maintenance cost and the dress of equipment per unit capacity
Put disposal costs sum；In the cycle lie that these expenses are divided to energy-storage system, so as to obtain the unit in planning horizon
Investment coefficient；
Coal consumption cost can be expressed as the quadratic function form of generated output；The power of conventional fired power generating unit and cogeneration units
Cost function is respectively：
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The constraints of the energy-storage system optimization collocation model of micro- energy net is as follows：
Electrical power Constraints of Equilibrium, heat supply Constraints of Equilibrium, wind power output constraint, Unit commitment, power storage system constraint and heat reservoir
Constraint；Bound that the Unit commitment is constrained including unit output bound, steam-extracting type unit heat is exerted oneself is constrained, steam-extracting type unit
The hot Climing constant of net generated output bound constraint, the general power Climing constant of unit and steam-extracting type unit；The constraint bar
The particular content of part is：
(1) electrical power Constraints of Equilibrium：
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In formula, NR is new energy quantity, PrthIt is the power that new energy is produced, PESSIt is the work(of energy-storage system charge or discharge
Rate, is charged as negative value, electric discharge be on the occasion of；Pload,thThe power for needed for the period load；
(2) heat supply Constraints of Equilibrium：
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(3) wind power output is constrained：
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And cut-out wind speed, vhtIt is the wind speed of certain period；
(4) Unit commitment：
1) unit output bound is constrained：
pi,min≤pi,t≤pi,max (13)
In formula, pi,min、pi,maxRespectively minimum of the unit under pure condensate operating mode, EIAJ；
2) thermoelectricity unit heat exert oneself upper and lower limit constraint：
0≤hi,t≤hi,max (14)
In formula, hi,maxThe threshold limit value exerted oneself for unit i heat, the value depends primarily on the size of capacity of heat exchanger；
3) the general power Climing constant of unit：
Pith-Pit(h-1)≤URi(1-yith)+Pi minyith (15)
Pith(h-1)-Pith≤DRi(1-zith)+Pi minzith (16)
In formula, URiIt is to ramp up limitation, yithBe the unit whether starting state amount, Pi minIt is the minimum hair of the generating set
Electricity, DRiIt is ramp down limitation, zithBe the unit whether stopped status amount；
4) the hot Climing constant of thermoelectricity unit：
hi,t-hi,t-1≤Δhu,i (17)
hi,t-1-hi,t≤Δhd,i (18)
In formula, Δ hu,i、Δhd,iThe maximum variable quantity of thermal power respectively in the steam-extracting type unit unit interval；
(5) power storage system is constrained：
Charging process is
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SOC (t) is the dump energy of energy-storage system at the end of t-th period；SOC (t-1) is energy storage at the end of the t-1 period
The dump energy of system；δ is the self-discharge rate of energy-storage system；Pc、PdThe respectively charge and discharge power of energy-storage system；ηc、ηdPoint
Not Wei power storage system charge and discharge efficiency；For the rated capacity of power storage system；
(6) heat reservoir is constrained：
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In formula, HHS(t) it is the hot stored energy capacitances of period t；μ is hot energy storage radiation loss rate；QHS_ch(t)、QHS_dis(t)And ηhch、ηhdis
Respectively period t suction heat release power and efficiency.
4. the micro- energy net energy-storage system of a kind of self according to claim 1 distributes method for solving rationally, its feature exists
In：In step 3, the use rain flow method estimation life of storage battery, its detailed process is as follows：
The life-span of energy-storage battery is by the depth of discharge including battery, high rate performance, discharge and recharge blanking voltage and environment temperature factor
Influence；Influence of the high rate performance to its life-span of battery is not considered, and the peak power of energy-storage battery takes rated value；Do not consider electricity
Influence of the discharge and recharge blanking voltage in pond to its life-span, sets the capacity span of energy-storage battery；Do not consider temperature to battery
The influence in life-span, environment temperature is considered as room temperature；The life-span of energy-storage battery is estimated using below equation：
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T=ceil (1/365.dloss) (23)
Wherein, dlossFor the electric energy-storage system life-span damage rate of one day, θ was periodic coefficient, and the complete period is 1, and the half period is 0.5；
CyciFor the corresponding maximum cycle of ith cycle period；T is life cycle, and ceil is flow in upper plenum；By building one
The charging and discharging curve of power storage system in it, it becomes possible to estimate the life-span of energy-storage system.
5. the micro- energy net energy-storage system of a kind of self according to claim 1 distributes method for solving rationally, its feature exists
In：In step 4, it is described algorithm to be chosen to go through according to Search Range or energy-storage system that particle cluster algorithm is sought to optimize is matched somebody with somebody
Put, its detailed process is as follows：
Configure scope according to energy-storage system to determine the method used, if scope is smaller, configuration categories are calculated using traversal more at least
Method；If scope is larger, the energy-storage system configuration for seeking to optimize using particle cluster algorithm draws the appearance of power storage system respectively
Capacity, the power of amount, power and heat reservoir；When Search Range is smaller, the energy storage system of self-sufficient Probability Condition is being met
It is under unified central planning to put in scope, choose one kind configuration and calculate cost, and judge whether to complete configuration used in given range, if completing given
In the range of the traversals of all configurations then complete calculating process.Do not complete to travel through a kind of calculating of configuration under then continuation；Such a side
Method is more comprehensive, it is adaptable to the less optimization problem of scope；
When Search Range is larger, particle swarm optimization algorithm is taken, the algorithm is a kind of optimization tool based on iteration；The algorithm
One group of RANDOM SOLUTION is started from, optimal solution is searched for by continuous iteration；In each iteration, by tracking individual extreme value and the overall situation
Extreme value carrys out Population Regeneration；The basic step of particle cluster algorithm is as follows：
1) position of each particulate and speed in random initializtion population；
2) fitness of each particulate is evaluated, the position of each current particulate and adaptive value are stored in the pbest of each particulate,
The position of adaptive value optimum individual and adaptive value in all pbest are stored in gbest；
3) formula h is usedi,t-hi,t-1≤Δhu,iThe speed of more new particle and displacement：
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3, continue search for.
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| CN108270244A (en) * | 2018-01-26 | 2018-07-10 | 国网辽宁省电力有限公司 | A kind of New-energy power system is in a variety of regulatory domain method of operation dispatching methods |
| CN108649553A (en) * | 2018-03-23 | 2018-10-12 | 中国电力科学研究院有限公司 | A kind of the configuration selection method and device of micro- energy net energy supplying system |
| CN109919478A (en) * | 2019-02-28 | 2019-06-21 | 天津大学 | A kind of comprehensive energy microgrid planing method considering comprehensive energy supply reliability |
| CN110323786A (en) * | 2018-03-28 | 2019-10-11 | 华北电力大学 | Dispatching method and device based on micro-capacitance sensor |
| CN110365058A (en) * | 2019-08-15 | 2019-10-22 | 中国人民解放军陆军勤务学院 | Isolated micro-capacitance sensor running optimizatin method based on simulated annealing particle swarm algorithm |
| CN110429642A (en) * | 2019-07-19 | 2019-11-08 | 昆明理工大学 | It is a kind of meter and renewable energy micro-capacitance sensor distributed scheduling method |
| CN111489020A (en) * | 2020-03-31 | 2020-08-04 | 杭州鸿晟电力设计咨询有限公司 | Independent type comprehensive energy grid electricity-gas energy storage system optimal configuration solving method |
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| CN110323786A (en) * | 2018-03-28 | 2019-10-11 | 华北电力大学 | Dispatching method and device based on micro-capacitance sensor |
| CN109919478A (en) * | 2019-02-28 | 2019-06-21 | 天津大学 | A kind of comprehensive energy microgrid planing method considering comprehensive energy supply reliability |
| CN110429642A (en) * | 2019-07-19 | 2019-11-08 | 昆明理工大学 | It is a kind of meter and renewable energy micro-capacitance sensor distributed scheduling method |
| CN110429642B (en) * | 2019-07-19 | 2022-07-12 | 昆明理工大学 | Micro-grid distributed scheduling method considering renewable energy |
| CN110365058A (en) * | 2019-08-15 | 2019-10-22 | 中国人民解放军陆军勤务学院 | Isolated micro-capacitance sensor running optimizatin method based on simulated annealing particle swarm algorithm |
| CN111489020A (en) * | 2020-03-31 | 2020-08-04 | 杭州鸿晟电力设计咨询有限公司 | Independent type comprehensive energy grid electricity-gas energy storage system optimal configuration solving method |
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| CN111523204B (en) * | 2020-03-31 | 2023-09-22 | 杭州鸿晟电力设计咨询有限公司 | Optimal configuration solving method for grid-connected comprehensive energy grid electricity-gas energy storage system |
| CN112488370A (en) * | 2020-11-20 | 2021-03-12 | 西安热工研究院有限公司 | Energy storage optimization method adopting demand side response planning |
| CN112488370B (en) * | 2020-11-20 | 2023-09-22 | 西安热工研究院有限公司 | Energy storage optimization method adopting demand side response planning |
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