CN110533225A - A kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming - Google Patents
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Abstract
The invention discloses a kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming.Business garden integrated energy system model is constructed first, on the basis of converting modeling to each element energy in garden, is considered the uncertainty of new energy power output and load, is established the energy-optimised scheduling model in business garden based on chance constrained programming.The model is with the minimum target of operating cost, it is solved using the hybrid algorithm of improved immune genetic algorithm and stochastic simulation, for unbalance risk adjoint in Chance-Constrained Programming Model, establish quantizating index, to provide reference for the integrated energy system traffic control of balanced economy and reliability, there is important directive significance for the actual schedule of system.
Description
Technical field
The present invention relates to energy-saving power generation dispatching technical field, in particular to a kind of business park based on chance constrained programming
Area's integrated energy system Optimization Scheduling.
Background technique
Traditional energy system operation is confined to the single energy form such as electricity, air and heat, cold, is unable to give full play between the energy
Mutual supplement with each other's advantages, coordinate using energy source, environmental protection, renewable energy consumption the problems such as meet with bottleneck.In response to this problem, energy
The concept of source interconnection net and integrated energy system is come into being, and the barrier between energy subsystem can be broken, in region, across
Interregional realization various energy resources complementation and collaboration optimization, are pushing the energy revolution of a new round.
Regional complex energy resource system towards garden refers to the load side positioned at the energy, is able to satisfy the energy in certain area
The a variety of energy production-supply-marketing integral systems with energy demand of terminal user, achieve in many American-European countries and successfully answer
With." reinforcing energy source interconnection, promote multipotency fusion and system complementary " was explicitly indicated in National Energy Board in 2015, and in 2017
Approve the construction of and push first batch of 23 " integrated optimization demonstration projects of providing multiple forms of energy to complement each other ".Single energy-provision way is compared in multipotency collaboration
Flexibility is had higher efficiency and energized, but also the complexity of energy resource system can be made to greatly increase, Optimized Operation is proposed
Higher requirement.
It is worth noting that, the load side resource of business garden integrated energy system aggregation is more, coupled between multiple-energy-source
It is even more serious, and be easy be influenced by user with energy rule.Existing research is excessively single on refrigeration model, does not consider
The participation of the complexity cooling equipment such as ice-storage air-conditioning, and not enough to the utilization of flexibility burdened resource in regional traffic net
Sufficiently.At the same time, the infiltration of new energy and load prediction accuracy it is not high to generation schedule formulate bring influence further
It highlights, increases the difficulty of system call.Therefore, it is badly in need of a kind of business garden integrated energy system Optimization Scheduling, with solution
The certainly above problem.
Summary of the invention
The purpose of the present invention is to provide a kind of, and the business garden integrated energy system based on chance constrained programming optimizes tune
Degree method, to solve the problems in background technique.
In order to achieve the above object, a kind of business garden integrated energy system based on chance constrained programming of the invention
Optimization Scheduling, the integrated energy system equipment modeling are based on intelligent distribution network, natural gas grid, confession in business garden
The characteristics of hot/cold net, network of communication lines close-coupled, mainly from the source of the energy, energy conversion and storage and end-user demands
Etc. each energy module in garden is modeled；
A kind of business garden integrated energy system Optimized Operation mathematical model based on chance constrained programming is built
It is vertical, it is to be optimized in the case where meeting a series of constraint conditions using system operation the lowest cost as target.Light is included in model
The uncertain factors such as power output, load prediction deviation are lied prostrate, are established with the chance constrained programming of the minimum objective function of operating cost
Model；
A kind of business garden integrated energy system Optimized Operation mathematical model based on chance constrained programming is asked
Solution is to consider that the complexity of model and optimization algorithm easily fall into local optimum and efficiency, is handled by stochastic simulation technology
Constraints condition of opportunity recycles improved immune genetic algorithm to solve.
Preferably, the integrated energy system energy module mainly include photovoltaic power generation equipment, cogeneration cooling heating system,
Electric boiler, ice-storage air-conditioning, heat storage water tank, electric car, bus electrical changing station etc., all kinds of energy module modelings are as follows:
1) cogeneration cooling heating system by gas turbine, waste heat boiler, absorption refrigeration unit at, output it is hot and cold,
There are strong coupled relation, model may be expressed as: electric three kinds of energy
In formula:The respectively power output, electrical efficiency and its rated power of t moment gas turbine, Respectively represent the thermal power of t moment co-generation system output, heat, the co-generation system of Absorption Refrigerator consumption
The cold power of output；A, b, c, d are gas turbine related coefficient；ηL、ηabsRespectively thermal loss coefficient and absorption refrigeration
Engine efficiency.
2) photovoltage model is as follows:
In formula:It contributes for photovoltaic generation unit t moment,For its rated power.
3) electric boiler model is as follows:
In formula: ηEHFor the transfer efficiency of electric boiler；The respectively electricity of t moment electric boiler consumption and production
Raw heat.
4) ice-storage air-conditioning model is as follows:
Daytime refrigeration mode, the constraint of ice-storage air-conditioning are as follows:
Above is respectively the power constraint of refrigeration machine, the electrical power of refrigeration consumption and the constraint of refrigeration machine working time；
For the refrigeration work consumption of refrigeration machine t moment；It is identified for t moment refrigeration work,It indicates in working condition, otherwise indicates to stop
Only work；Indicate the electrical power of t moment refrigeration consumption；COPEIt is the refrigeration efficiency ratio of unit；TvalleyRefer to electricity price low ebb
Period.
Night ice-make mode, ice-storage air-conditioning constraint are as follows:
It is above respectively that refrigeration machine ice-making capacity, ice making institute's power consumption and ice making work time-constrain, ice making need
Electricity price low-valley interval is carried out continuously, in formula:The respectively electrical power of t moment ice making power and ice making consumption；For t
Moment, iceman made a check mark,It indicates in working condition, otherwise indicates to stop working.
Ice-melt on daytime refrigeration constraint:
Ice amount expression formula respectively in ice-melt power constraint, ice-melt time-constrain and Ice Storage Tank above, whereinFor t
Moment ice-melt power,It works and identifies for t moment ice-melt,It indicates in working condition, otherwise indicates to stop working；ISt+1
Indicate the ice amount in t+1 moment Ice Storage Tank, σiIt is Ice Storage Tank from loss factor, ηisAnd ηimFor ice-reserving efficiency and ice-melt efficiency.
5) heat storage water tank model is as follows:
In formula,Indicate quantity of heat storage in t+1 moment water tank；ηm、ηwFor heat accumulation, exothermal efficiency；For heat release mark, it is
1 indicates heat release, and 0 indicates heat accumulation；For the storage thermal power of t moment water tank, maximum value isWhen water tank heat releaseIt is positive, when heat accumulationIt is negative；
6) electric car model is as follows:
Each EV power and electric quantity change may be expressed as:
In formula:For the power of t moment kth electric car,For the electricity of t+1 moment kth electric car
Amount,WithThe respectively charge and discharge rated power of kth electric car；ηc、ηdFor charge and discharge efficiency；When for t
The electric discharge mark of kth electric car is carved,It indicates electric discharge, otherwise is charging.
It, which runs, meets following constraint:
The above period constraint that is respectively the Constraint of electric car, can be scheduled and electric car leave garden
Electricity requirement when area；Wherein, battery capacity minimum valueAnd maximum value20%E is taken respectivelybatAnd 90%Ebat, Ebat
For the battery specified electric quantity of electric car；It is that kth electric car reaches garden and leaves the garden time respectively；The
K electric car leaves electricity when gardenNeed to meet trip distance dkleaRequirement, dmaxIt is continuous for electric car maximum
Navigate mileage.
7) electric bus electrical changing station model is as follows:
Electric bus electrical changing station power and electricity expression formula are as follows:
In formula:It is the electric bus electrical changing station power of t moment,Electric bus for the t+1 moment changes electricity
The electricity stood, Δ t are charging time amount；pcBSS、pdBSSIt is the charge/discharge power of charge position；It is i-th of charge position of t moment
Electric discharge mark, take 1 expression discharge, 0 indicate charging；It is charge/discharge efficiency respectively；Nc、Ns,tRespectively charge
Position number and t moment change battery number；The residual electric quantity of used batteries is changed for t moment；It is newly changed for t moment
Every piece of battery capacity；Meet following constraint when it runs:
The power constraint and Constraint of electrical changing station, minimum amount of power are respectively indicated aboveChanging for the moment need to be met
Electricity demanding and the minimum electricity of other batteries, if newly changing battery SOC is 0.9；Maximum electricityTaking SOC for all batteries is 0.9
When total electricity, EbssFor the specified electric quantity of every battery, SOC is between 0.2~0.9；NzFor total number of batteries.
Preferably, the system optimization scheduling mathematic model includes objective function, constraint condition and chance constrained programming mould
Type.Using the model using system, cost minimization is target within a dispatching cycle, including purchase gas cost, purchases strategies and benefit
Cost is repaid, objective function is expressed as follows:
In formula:Respectively t moment purchase gas cost, purchases strategies and cost of compensation；Cgas、CVgasFor
Gas Prices and its calorific value；Indicate the interaction power of t moment system and power grid, positive value, which represents, buys electricity, and negative value representative is sold
Electricity；Respectively t moment power purchase, sale of electricity price；ηbPower purchase is indicated to buy electric mark, 1, and 0 indicates to sell electricity；It is pair
The compensation of ice-storage air-conditioning ice-melt cooling supply and electric car, electrical changing station electric discharge, NEFor the number of electric car, λ1It is repaid for cold-patch
Coefficient, electronic compensating coefficient is related with t moment electricity price, is taken as
Preferably, consider photovoltaic and hot and cold, electric load uncertainty, M field is obtained by Latin Hypercube Sampling
Photovoltaic power output and workload demand under scape, and the mistake load risk under different scenes is quantified.Under some scene, work as machine
When group power output is greater than workload demand, losing load is 0；On the contrary, the power loss under i-th of scene of t moment is negative when supply falls short of demand
Charged expression are as follows:
In formula:Respectively t moment expectation electric load and desired photovoltaic are contributed,It is i-th of t moment
Electric load prediction error and photovoltaic power generation output forecasting error under scene；
T moment system power loss load is being averaged under M scene, be may be expressed as:
It is noted that heat, the mistake load of refrigeration duty are similar with electric load mistake load, it is being averaged under M scene
Load is lost, is no longer listed as space is limited herein.The risk cost that business garden faces within dispatching cycle may be expressed as:
In formula: QNSt、CNStIt is that t moment thermic load loses load and refrigeration duty loses load, C respectivelyer,t、Cqr,t、 Ccr,t
For t moment system power loss, heat, the corresponding unit cost of refrigeration duty.
Preferably, the model constraint condition further includes power-balance, reliability and power grid in addition to equipment operation constraint
Interaction constraint etc.:
1) power-balance constraint
In formula:Respectively t moment electricity, heat, refrigeration duty demand.
2) constraint is interacted with power grid
In formula:Power is interacted with power grid for t moment system,For maximum interaction power；
3) electricity of system, heat, cold reliability constraint:
In formula: β is confidence level, is set as 0.95.
Preferably due to the model decision variable is more and each variable between relationship is complicated, intercouples, Zhi Nengtong
Stochastic simulation technology processing constraints condition of opportunity is crossed, improved immune genetic algorithm is recycled to solve based on chance constrained programming
Business garden integrated energy system Optimal Operation Model.
For one group of given decision variable, stochastic simulation is for examining whether constraints condition of opportunity meets.For the moment
T first sets counter N '=0；Then random quantity is generated using Latin Hypercube simulation And become with decision
Amount substitutes into the left side of reliability constraint inequality together, if inequality is set up, N '=N '+1 is so repeated M times.If M is sufficient
Enough big, according to law of great number, when N '/M >=β, reliability constraint is set up, and one group of decision variable at this time is feasible
Solution, then selects optimal solution in feasible solution.
The process of the immune vaccine algorithm is as follows:
(1) analysis optimization target and and its constraint equation, determine suitable coding form, herein use real coding.
(2) meeting unit output constraint and trading under constraint condition with power grid, N number of antibody is being randomly generated and from memory
M antibody is extracted in library constitutes initial population.
(3) the expectation breeding potential of population antibody is evaluated, compared to the fitness evaluation of traditional immunization genetic algorithm,
The index had not only encouraged the antibody of fitness high (scheduling cost is small), but also inhibited the antibody of concentration high (similarity is high), it is ensured that
Antibody diversity.
(4) population is subjected to descending sort by desired breeding potential, the antibody for taking top n outstanding constitutes parent group, simultaneously
Preceding m elite antibody is stored in data base.
(5) judge whether to meet termination condition, be, terminate；Otherwise carry out next step operation.
(6) based on step (4) result antibody is selected, is intersected, mutation operation, the group after being made a variation.
(7) evaluation of average expectation breeding potential is carried out to the population after variation behaviour, the population that preset value is not achieved is planted
Group's cutting operation, antibody is ranked up by desired breeding potential size, again to the antibody lower than population average expectation breeding potential
Mutation operation is carried out, the antibody after making a variation again and the antibody for being not carried out again mutation operation are merged into new population.
(8) turn to go to execute step (3), until output result.
Business garden integrated energy system Optimization Scheduling based on chance constrained programming of the invention has as follows
Benefit:
(1) electric car and bus electrical changing station can discharge as far as possible in charging more than the electricity price paddy period, peak period, the charge and discharge
Electric strategy can improve the economy and flexibility of scheduling under the premise of not influencing user's trip comfort, be electric system
The development of flexibility resource provides huge space.
(2) pass through the optimal control to ice-storage air-conditioning, it is possible to reduce system operation cost, the demand for improving system are rung
Answer potentiality.Ice-storage air-conditioning can play the role of peak load shifting in grid side, can improve electricity consumption economy in user side.
(3) confidence level had not only influenced the operating cost of system, but also influenced its risk level.β is smaller, indicates to reliability
Constraint requirements are lower, and operating cost reduces at this time.But lower, risk increase is managed to uncertain factor.It is capable of providing one herein
The suitable β of a combined reliability and cost-effectiveness requirement.
Detailed description of the invention:
Fig. 1 is a kind of business garden integrated energy system structural schematic diagram based on chance constrained programming.
Fig. 2 is that photovoltaic goes out in a kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming
The desired value curve of power and cold and hot electric load.
Fig. 3 is a kind of business garden integrated energy system Optimization Scheduling flow chart based on chance constrained programming.
Fig. 4 is two kinds of moulds in a kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming
Electric car and electrical changing station scheduling result figure under formula.
Fig. 5 is two kinds of moulds in a kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming
Power diagram is interacted with power grid under formula.
Fig. 6 be electricity in a kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming, heat,
Refrigeration duty scheduling result figure.
Specific embodiment:
To keep the purposes, technical schemes and advantages of the invention implemented clearer, below in conjunction in the embodiment of the present invention
Attached drawing, technical solution in the embodiment of the present invention is further described in more detail.In the accompanying drawings, identical from beginning to end or
Similar label indicates same or similar element or element with the same or similar functions.Described embodiment is this
Invention a part of the embodiment, instead of all the embodiments.The embodiments described below with reference to the accompanying drawings are exemplary,
It is intended to be used to explain the present invention, and is not considered as limiting the invention.Based on the embodiments of the present invention, this field is general
Logical technical staff every other embodiment obtained without creative efforts, belongs to protection of the present invention
Range.The embodiment of the present invention is described in detail with reference to the accompanying drawing.
A kind of business garden integrated energy system tune based on chance constrained programming of a broad embodiment according to the present invention
Degree method, the business garden integrated energy system Optimization Scheduling includes integrated energy system equipment modeling, Optimized Operation
The foundation of mathematical model and the solution of Optimized Operation mathematical model.The comprehensive energy in business garden based on chance constrained programming
The equipment modeling of source system, in conjunction with business garden intelligent distribution network, natural gas grid, for hot/cold net, network of communication lines close-coupled
Feature, mainly from the source of the energy, energy conversion and storage and end-user demands etc. to each energy module in garden into
Row modeling；
The foundation of the business garden integrated energy system Optimal Operation Model based on chance constrained programming, optimization are adjusted
Model is spent in the case where meeting a series of constraint conditions, is optimized using system operation the lowest cost as target.In addition, in model
The uncertain factors such as photovoltaic power output, load prediction deviation are included in, are established with the minimum target of integrated energy system operating cost
The Chance-Constrained Programming Model of function；
The solution of the business garden integrated energy system Optimized Operation mathematical model based on chance constrained programming, it is excellent
Change that scheduling model decision variable is more and relationship is complicated, intercouples between each variable, considers that the complexity of model and optimization are calculated
Method easily falls into local optimum and efficiency, handles constraints condition of opportunity by stochastic simulation technology, recycles improved immune
Genetic algorithm solves.
Business garden integrated energy system Optimization Scheduling based on chance constrained programming of the invention is to realize system
The minimum target of totle drilling cost, while having comprehensively considered risk and economy in scheduling, peak clipping is carried out to grid load curve
It is valley-fill.
As shown in Figure 1, equipment master in the business garden integrated energy system Optimized Operation based on chance constrained programming
Electricity is changed including photovoltaic power generation, cogeneration cooling heating system, electric boiler, ice-storage air-conditioning, heat storage water tank, electric car, bus
It stands, all kinds of energy module modelings are as follows:.
1) cogeneration cooling heating system by gas turbine, waste heat boiler, absorption refrigeration unit at, output it is hot and cold,
There are strong coupled relation, model may be expressed as: electric three kinds of energy
In formula:The respectively power output, electrical efficiency and its rated power of t moment gas turbine, Respectively represent the thermal power of t moment co-generation system output, heat, the co-generation system of Absorption Refrigerator consumption
The cold power of output；A, b, c, d are gas turbine related coefficient；ηL、ηabsRespectively thermal loss coefficient and absorption refrigeration
Engine efficiency.
2) photovoltage model is as follows:
In formula:It contributes for photovoltaic generation unit t moment,For its rated power.
3) electric boiler model is as follows:
In formula: ηEHFor the transfer efficiency of electric boiler；The respectively electricity of t moment electric boiler consumption and generation
Heat.
4) ice-storage air-conditioning model is as follows:
Daytime refrigeration mode, the constraint of ice-storage air-conditioning are as follows:
Above is respectively the power constraint of refrigeration machine, the electrical power of refrigeration consumption and the constraint of refrigeration machine working time；
For the refrigeration work consumption of refrigeration machine t moment；It is identified for t moment refrigeration work,It indicates in working condition, otherwise indicates to stop
Only work；Indicate the electrical power of t moment refrigeration consumption；COPEIt is the refrigeration efficiency ratio of unit；TvalleyRefer to electricity price low ebb
Period.
Night ice-make mode, ice-storage air-conditioning constraint are as follows:
It is above respectively that refrigeration machine ice-making capacity, ice making institute's power consumption and ice making work time-constrain, ice making need
Electricity price low-valley interval is carried out continuously, in formula:The respectively electrical power of t moment ice making power and ice making consumption；For t
Moment, iceman made a check mark,It indicates in working condition, otherwise indicates to stop working.
Ice-melt on daytime refrigeration constraint:
Ice amount expression formula respectively in ice-melt power constraint, ice-melt time-constrain and Ice Storage Tank above, whereinFor t
Moment ice-melt power,It works and identifies for t moment ice-melt,It indicates in working condition, otherwise indicates to stop working；ISt+1
Indicate the ice amount in t+1 moment Ice Storage Tank, σiIt is Ice Storage Tank from loss factor, ηisAnd ηimFor ice-reserving efficiency and ice-melt efficiency.
5) heat storage water tank model is as follows:
In formula,Indicate quantity of heat storage in t+1 moment water tank；ηm、ηwFor heat accumulation, exothermal efficiency；For heat release mark, it is
1 indicates heat release, and 0 indicates heat accumulation；For the storage thermal power of t moment water tank, maximum value isWhen water tank heat releaseIt is positive, when heat accumulationIt is negative.
6) electric car model is as follows:
Each EV power and electric quantity change may be expressed as:
In formula:For the power of t moment kth electric car,For the electricity of t+1 moment kth electric car
Amount,WithThe respectively charge and discharge rated power of kth electric car；ηc、ηdFor charge and discharge efficiency；When for t
The electric discharge mark of kth electric car is carved,It indicates electric discharge, otherwise is charging.
It, which runs, meets following constraint:
The above period constraint that is respectively the Constraint of electric car, can be scheduled and electric car leave garden
Electricity requirement when area；Wherein, battery capacity minimum valueAnd maximum value20%E is taken respectivelybatAnd 90%Ebat, Ebat
For the battery specified electric quantity of electric car；It is that kth electric car reaches garden and leaves the garden time respectively；The
K electric car leaves electricity when gardenNeed to meet trip distance dkleaRequirement, dmaxIt is continuous for electric car maximum
Navigate mileage.
7) electric bus electrical changing station model is as follows:
Electric bus electrical changing station power and electricity expression formula are as follows:
In formula:It is the electric bus electrical changing station power of t moment,Electric bus for the t+1 moment changes electricity
The electricity stood, Δ t are charging time amount；pcBSS、pdBSSIt is the charge/discharge power of charge position；It is i-th of charge position of t moment
Electric discharge mark, take 1 expression discharge, 0 indicate charging；It is charge/discharge efficiency respectively；Nc、Ns,tRespectively charge
Position number and t moment change battery number；The residual electric quantity of used batteries is changed for t moment；It is newly changed for t moment
Every piece of battery capacity；Meet following constraint when it runs:
The power constraint and Constraint of electrical changing station, minimum amount of power are respectively indicated aboveChanging for the moment need to be met
Electricity demanding and the minimum electricity of other batteries, if newly changing battery SOC is 0.9；Maximum electricityTaking SOC for all batteries is 0.9
When total electricity, EbssFor the specified electric quantity of every battery, SOC is between 0.2~0.9；NzFor total number of batteries.
As shown in Fig. 2, the expectation curve of hot and cold, electric load needed for business garden and photovoltaic power output, super by Latin
Cube simulation load prediction deviation and photovoltaic output deviation, deviation is superimposed with corresponding desired value, as actually hot and cold, electric
Load and photovoltaic power output.
Optimal Operation Model using system within a dispatching cycle cost minimization as target, including purchase gas cost, power purchase at
This and cost of compensation, objective function are expressed as follows:
In formula:Respectively t moment purchase gas cost, purchases strategies and cost of compensation；Cgas、CVgasFor
Gas Prices and its calorific value；Indicate the interaction power of t moment system and power grid, positive value, which represents, buys electricity, and negative value representative is sold
Electricity；Respectively t moment power purchase, sale of electricity price；ηbPower purchase is indicated to buy electric mark, 1, and 0 indicates to sell electricity；It is pair
The compensation of ice-storage air-conditioning ice-melt cooling supply and electric car, electrical changing station electric discharge, NEFor the number of electric car, λ1It is repaid for cold-patch
Coefficient, electronic compensating coefficient is related with t moment electricity price, is taken as
Consider photovoltaic and hot and cold, electric load uncertainty, photovoltaic under M scene is obtained by Latin Hypercube Sampling
Power output and workload demand, and the mistake load risk under different scenes is quantified.Under some scene, when unit output is big
When workload demand, losing load is 0；On the contrary, the power loss load electricity under i-th of scene of t moment can table when supply falls short of demand
It is shown as:
In formula:Respectively t moment expectation electric load and desired photovoltaic are contributed,It is i-th of t moment
Electric load prediction error and photovoltaic power generation output forecasting error under scene；
T moment system power loss load is being averaged under M scene, be may be expressed as:
It is noted that heat, the mistake load of refrigeration duty are similar with electric load mistake load, it is being averaged under M scene
Load is lost, is no longer listed as space is limited herein.The risk cost that business garden faces within dispatching cycle may be expressed as:
In formula: QNSt、CNStIt is that t moment thermic load loses load and refrigeration duty loses load, C respectivelyer,t、Cqr,t、 Ccr,t
For t moment system power loss, heat, the corresponding unit cost of refrigeration duty.
The model constraint condition further includes power-balance, reliability, interacts about with power grid in addition to equipment operation constraint
Beam etc.:
1) power-balance constraint
In formula:Respectively t moment electricity, heat, refrigeration duty demand.
2) constraint is interacted with power grid
In formula:Power is interacted with power grid for t moment system,For maximum interaction power；
3) electricity of system, heat, cold reliability constraint:
In formula: β is confidence level, is set as 0.95.
Above-mentioned model is difficult to convert into deterministic models solution, therefore, handles chance constraint item by stochastic simulation technology
Part recycles improved immune genetic algorithm to solve the business garden integrated energy system optimization based on chance constrained programming and adjusts
Spend model.
For one group of given decision variable, stochastic simulation is for examining whether constraints condition of opportunity meets.For the period
T first sets counter N '=0；Then random quantity is generated using Latin Hypercube simulation And become with decision
Amount substitutes into the left side of reliability constraint inequality together, if inequality is set up, N '=N '+1 is so repeated M times.If M is sufficient
Enough big, according to law of great number, when N '/M >=β, reliability constraint is set up, and one group of decision variable at this time is feasible
Solution, then selects optimal solution using improved immune genetic algorithm in feasible solution.
As shown in figure 3, the process of the immune vaccine algorithm is as follows:
(1) analysis optimization target and and its constraint equation, determine suitable coding form, herein use real coding.
(2) meeting unit output constraint and trading under constraint condition with power grid, N number of antibody is being randomly generated and from memory
M antibody is extracted in library constitutes initial population.
(3) the expectation breeding potential of population antibody is evaluated, compared to the fitness evaluation of traditional immunization genetic algorithm,
The index had not only encouraged the antibody of fitness high (scheduling cost is small), but also inhibited the antibody of concentration high (similarity is high), it is ensured that
Antibody diversity.
(4) population is subjected to descending sort by desired breeding potential, the antibody for taking top n outstanding constitutes parent group, simultaneously
Preceding m elite antibody is stored in data base.
(5) judge whether to meet termination condition, be, terminate；Otherwise carry out next step operation.
(6) based on step (4) result antibody is selected, is intersected, mutation operation, the group after being made a variation.
(7) evaluation of average expectation breeding potential is carried out to the population after variation behaviour, the population that preset value is not achieved is planted
Group's cutting operation, antibody is ranked up by desired breeding potential size, again to the antibody lower than population average expectation breeding potential
Mutation operation is carried out, the antibody after making a variation again and the antibody for being not carried out again mutation operation are merged into new population.
(8) turn to go to execute step (3), until output result.
In one embodiment, business garden is by 1 3000KW gas turbine, 1 1300KW photovoltaic facility, 1
Electric boiler, 1 ice-storage air-conditioning of the heat storage water tank of 3000KWh, 1 1000KW, 1 bus electrical changing station and 100 it is identical
Model electric car composition.Gas turbine parameter a, b, c, d distinguish value be -0.3144,0.076554,0.4285,
0.09122；Assuming that night-time hours (when 1-6,23-24) change battery number obey mean value be 2.5, the normal distribution that standard deviation is 1,
That is Ns~N (2.5,12), daytime period changes electricity demanding height, meets Ns~N (5,1.52), change the SOC of battery [0.25,
0.35] it is uniformly distributed in；The time of EV arrival gardenSOC is uniformly distributed in [0.2,0.4] when arrival；
Leave the garden time And the trip distance d that comes off dutykleaLogarithm normal distribution is obeyed, lnd is metklea~N
(3.65,0.42), dmaxFor 80km；Electrical changing station change per hour electricity demanding number, every piece change the SOC of battery, each EV reach garden
Time and SOC value are left garden time and next trip distance and are obtained by Monte Carlo simulation, system and power grid
Maximum interaction power is 800KW, and related energy prices are shown in Table 1；The standard deviation of the prediction deviation of photovoltaic power output and cold and hot electric load
It is 0.06；Device parameter is shown in Table 2 and table 3.
1 Beijing area general industry energy prices of table
Wherein, peak period is (11:00-15:00；19:00-21:00), usually section is (8:00-10:00；16:00-
18:00；22:00-23:00), the paddy period is (24:00-7:00), and selling electricity price lattice is 0.8 times for buying electricity price lattice；Heating value of natural gas
Take 9.5KWh/m3。
2 device parameter of table
3 energy-storage system device parameter of table
It is solved using improved immune genetic algorithm, obtains decision variable Responded to probe into different demands in garden (demand response,
DR) to the influence of optimum results, it is as follows to construct DR mode: electric car and electrical changing station are according to the orderly charge and discharge of tou power price excitation
Electricity, and ice-storage air-conditioning undertakes part refrigeration duty.With inactive DR (electric car and the unordered charge and discharge of electrical changing station and ice storage
Air-conditioning does not work) result compare and analyze.Fig. 4 is that electric car and electrical changing station scheduling result, Fig. 5 are under different mode
Under different mode with power grid interaction results.There are electricity under DR participation mode, heat, the scheduling result of refrigeration duty to see Fig. 6.
In Fig. 4, electric car and electrical changing station power positive value represent electric discharge, and negative value represents charging.As can be seen that in timesharing
Under Price Mechanisms and compensation incentive, the charge and discharge of the two are optimized.Without under DR mode, electric car is reached behind garden just
It starts to charge, until stopping after trip institute's electricity demand, therefore when charge power concentrates on 7-10, the moment is just located always later
In idle state, unordered charge and discharge limit electric car and participate in the space that electric system flexibility is adjusted.And under DR mode,
Electric car meet user go on a journey rule on the basis of, when electricity price is low more charge, when electricity price peak, puts extra electricity
Out, as long as guaranteeing that electricity reaches institute's electricity demand when leaving garden.Similarly, under no DR, the electrical power of electrical changing station is the moment
Change electricity demanding power, and in DR mode, electrical changing station can also carry out orderly charge and discharge according to tou power price, next meeting
Under the premise of hour changes electricity demanding, improve network load characteristic.
Fig. 5 is the interactive quantity of system and power grid, and positive value indicates to buy electricity, and electricity is sold in negative value expression.System is one under both of which
Interactive quantity total with power grid is almost the same in it, and since electricity price motivates, middle electric car and electrical changing station load occur under DR mode
Translation, therefore cause to buy electricity and change also based on electricity price.It is mainly manifested in: due to electric car and changing electricity under DR mode
It stands the adjustings of orderly charge and discharge, system is realized in electricity price peak period by buying electricity to the conversion for selling electricity, overall in low-valley interval
Electricity of buying increased.Play the role of peak load shifting to grid load curve.Table 4 be both of which under operation at
This comparison, it can be seen that DR mode can not only improve load curve, moreover it is possible to improve the economy of scheduling.
Operating cost under 4 both of which of table
Fig. 6 is the scheduling result of electric load under DR mode, thermic load and refrigeration duty.Gas turbine goes out in electric load scheduling
Power is in a higher level, undertakes most of electrical load requirement.Electric car and bus electrical changing station are as mobile storage
Can, the economy and flexibility of system call are improved under the premise of not influencing user's trip comfort, are that electric system is flexible
Property resource development huge space is provided.The waste heat of gas turbine assumes responsibility for most thermal load demands in thermic load scheduling,
Heat storage water tank can store heat when heat surplus, heat release when shortage of heat, and it is flat to play good heat
Shifting effect.Electric heating and heat accumulation improve the thermoelectricity decoupling ability of gas turbine, can realize the confession of thermic load more flexiblely
It answers.Absorption Refrigerator assumes responsibility for most refrigeration duty demand in refrigeration duty scheduling, and ice-storage air-conditioning is at 24~next day 7
In ice-make condition, cooling capacity is stored in the form of ice, at refrigeration duty peak period and higher electricity price (predominantly 11
When~15) ice-melt progress cooling supply, reduces peak times of power consumption air conditioner load.At 24, ice amount and the most first ice amount of Ice Storage Tank are held
It is flat, it is 324KWh.
The influence that cost and system risk level are dispatched to inquire into confidence level to integrated energy system, risk is punished
Coefficient Cer,t、Cqr,t、Ccr,tIt is set to1.3,2, confidence level is successively reduced to 0.8 from 1 with 0.05 amplitude,
Operating cost and risk of the solving system under different confidence levels, as a result as shown in table 5.
System call cost and risk under the different confidence levels of table 5
As can be seen from Table 5: integrated energy system economic load dispatching cost is constantly reduced with the reduction of confidence level β, wind
This is become by inches as β reduction is continuously increased, and the trend risen after falling before is then presented in the totle drilling cost after meter and risk.Thus may be used
See, under the conditions of chance constrained programming, system low cost and low-risk cannot be taken into account.In a sense 1- β is system
The maximum load-loss probability of permission.Therefore, it is necessary to choose a suitable β, carry out system between economy and risk
Reasonable compromise.In table 2, as β=0.9, the cost after meter and risk is minimum, and economy and reliability after tradeoff are most
It is good.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate examples made by the present invention, and is not to this
The restriction of the embodiment of invention.It for those of ordinary skill in the art, on the basis of the above description can be with
It makes other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.And these belong to
It is still in the protection scope of this invention in the obvious changes or variations that spirit of the invention is extended out.
Claims (9)
1. a kind of business garden integrated energy system Optimization Scheduling based on chance constrained programming characterized by comprising
1) integrated energy system equipment modeling: intelligent distribution network, natural gas grid, for hot/cold net, network of communication lines close-coupled, from energy
Source, conversion and the storage in source and the aspect of end-user demands model each energy resource system equipment in garden；
2) foundation of Optimized Operation mathematical model: Optimal Operation Model is optimized using system operation the lowest cost as target；
Be included in the uncertain factors such as photovoltaic power output, load prediction deviation in the mathematical model, establish with integrated energy system operation at
The Chance-Constrained Programming Model of this minimum objective function；
3) solution of Optimized Operation mathematical model: there are multiple decision variables for Optimal Operation Model, and intercouple between variable；
Constraints condition of opportunity is handled by stochastic simulation technology, improved immune genetic algorithm is recycled to solve.
2. the business garden integrated energy system Optimization Scheduling according to claim 1 based on chance constrained programming,
It is characterized by: the integrated energy system equipment includes photovoltaic power generation equipment, cogeneration cooling heating system, electric boiler, ice storage
Air-conditioning, heat storage water tank, electric car, bus electrical changing station equipment；
Affiliated integrated energy system equipment modeling is as follows:
1) cogeneration cooling heating system is by gas turbine, waste heat boiler, absorption refrigeration unit at hot and cold, electric three kinds of output
There are coupled relation, models to be expressed as energy:
In formula:PgtNThe respectively power output, electrical efficiency and its rated power of t moment gas turbine,Respectively represent the thermal power of t moment co-generation system output, the heat of Absorption Refrigerator consumption, coproduction
The cold power of system output；A, b, c, d are gas turbine related coefficient；ηL、ηabsRespectively thermal loss coefficient and absorption system
Cold efficiency；
2) model of photovoltaic power generation equipment is as follows:
In formula: PvtIt contributes for photovoltaic generation unit t moment,For its rated power；
3) electric boiler model is as follows:
In formula: ηEHFor the transfer efficiency of electric boiler；The respectively heat of the electricity of t moment electric boiler consumption and generation
Amount；
4) ice-storage air-conditioning model is as follows:
Daytime refrigeration mode, the constraint of ice-storage air-conditioning are as follows:
Above is respectively the power constraint of refrigeration machine, the electrical power of refrigeration consumption and the constraint of refrigeration machine working time；For system
The refrigeration work consumption of cold t moment；It is identified for t moment refrigeration work,It indicates in working condition, otherwise indicates to stop work
Make；Indicate the electrical power of t moment refrigeration consumption；COPEIt is the refrigeration efficiency ratio of unit；TvalleyRefer to electricity price low-valley interval；
Night ice-make mode, ice-storage air-conditioning constraint are as follows:
It is above respectively that refrigeration machine ice-making capacity, ice making institute's power consumption and ice making work time-constrain, ice making are needed in electricity price
Low-valley interval is carried out continuously, in formula:The respectively electrical power of t moment ice making power and ice making consumption；For t moment
Iceman makes a check mark,It indicates in working condition, otherwise indicates to stop working；
Ice-melt on daytime refrigeration constraint:
Ice amount expression formula respectively in ice-melt power constraint, ice-melt time-constrain and Ice Storage Tank above, whereinMelt for t moment
Ice power,It works and identifies for t moment ice-melt,It indicates in working condition, otherwise indicates to stop working；ISt+1It indicates in t
The ice amount of+1 moment Ice Storage Tank, σiIt is Ice Storage Tank from loss factor, ηisAnd ηimFor ice-reserving efficiency and ice-melt efficiency；
5) heat storage water tank model is as follows:
In formula,For quantity of heat storage in t+1 moment water tank；ηm、ηwFor heat accumulation, exothermal efficiency；For heat release mark, indicate to put for 1
Heat, 0 indicates heat accumulation；For the storage thermal power of t moment water tank, maximum value Qwtmax, when water tank heat releaseIt is positive,
When heat accumulationIt is negative；
6) electric car model is as follows:
The power and electric quantity change of each electric car may be expressed as:
In formula:For the power of t moment kth electric car,For the electricity of t+1 moment kth electric car,
WithThe respectively charge and discharge rated power of kth electric car；ηc、ηdFor charge and discharge efficiency；For t moment kth
The electric discharge of electric car identifies,It indicates electric discharge, otherwise indicates charging；
It, which runs, meets following constraint:
When the above period constraint that is respectively the Constraint of electric car, can be scheduled and electric car leave garden
Electricity requirement；Wherein, battery capacity minimum valueAnd maximum value20%E is taken respectivelybatAnd 90%Ebat, EbatFor electricity
The battery specified electric quantity of electrical automobile； It is that kth electric car reaches garden and leaves the garden time respectively；Kth electricity
Electrical automobile leaves electricity when gardenNeed to meet trip distance dkleaRequirement, dmaxFor in the continuation of the journey of electric car maximum
Journey；
7) electric bus electrical changing station model is as follows:
Electric bus electrical changing station power and electricity expression formula are as follows:
In formula:It is the electric bus electrical changing station power of t moment,For the electric bus electrical changing station at t+1 moment
Electricity, Δ t are charging time amount；pcBSS、pdBSSIt is the charge/discharge power of charge position；It is putting for i-th of charge position of t moment
Electricity mark, takes 1 expression to discharge, and 0 indicates charging；It is charge/discharge efficiency respectively；Nc、Ns,tRespectively charge position
It is several and t moment to change battery number；The residual electric quantity of used batteries is changed for t moment；Every piece newly changed for t moment
Battery capacity；It needs to meet following constraint when it runs:
The power constraint and Constraint of electrical changing station, minimum amount of power are respectively indicated aboveThe moment need to be met changes electricity demanding
With the minimum electricity of other batteries, if newly change battery SOC be 0.9；Maximum electricityIt is total when for all batteries, to take SOC be 0.9
Electricity, EbssFor the specified electric quantity of every battery, SOC is between 0.2~0.9；NzFor total number of batteries.
3. the business garden integrated energy system Optimization Scheduling according to claim 1 based on chance constrained programming,
It is characterized by: the energy source terminal demand includes hot and cold, electric load needed for business garden.
4. the business garden integrated energy system Optimization Scheduling according to claim 1 based on chance constrained programming,
It is characterized by: the system optimization scheduling mathematic model includes objective function, constraint condition and Chance-Constrained Programming Model.
5. the business garden integrated energy system Optimization Scheduling according to claim 4 based on chance constrained programming,
It is characterized by: the model using system within a dispatching cycle cost minimization as target, including purchase gas cost, purchases strategies
And cost of compensation, objective function are expressed as follows:
In formula:Respectively t moment purchase gas cost, purchases strategies and cost of compensation；Cgas、CVgasFor natural gas
Price and its calorific value；Indicate the interaction power of t moment system and power grid, positive value, which represents, buys electricity, and electricity is sold in negative value representative；Respectively t moment power purchase, sale of electricity price；ηbPower purchase is indicated to buy electric mark, 1, and 0 indicates to sell electricity；It is to be stored to ice
The compensation of cold air-conditioning ice-melt cooling supply and electric car, electrical changing station electric discharge, NEFor the number of electric car, λ1For cold penalty coefficient,
Electronic compensating coefficient is related with t moment electricity price, is taken as
6. the business garden integrated energy system Optimization Scheduling according to claim 4 based on chance constrained programming,
It is characterized by: considering photovoltaic and hot and cold, electric load uncertainty, light under M scene is obtained by Latin Hypercube Sampling
Volt power output and workload demand, and the mistake load risk under different scenes is quantified；Under some scene, when unit output is big
When workload demand, losing load is 0；On the contrary, the power loss load under i-th of scene of t moment indicates when supply falls short of demand
Are as follows:
In formula:Respectively t moment expectation electric load and desired photovoltaic are contributed,For under i-th of scene of t moment
Electric load predicts error and photovoltaic power generation output forecasting error；
T moment system power loss load is being averaged under M scene, is indicated are as follows:
The risk cost that business garden faces within dispatching cycle indicates are as follows:
In formula: QNSt、CNStIt is that t moment thermic load loses load and refrigeration duty loses load, C respectivelyer,t、Cqr,t、Ccr,tWhen for t
The corresponding unit cost of etching system power loss, heat, refrigeration duty.
7. the business garden integrated energy system Optimization Scheduling according to claim 4 based on chance constrained programming,
It is characterized by: the model constraint condition further includes power-balance, reliability, interacts with power grid in addition to equipment operation constraint
Constraint:
1) power-balance constraint
In formula:Respectively t moment electricity, heat, refrigeration duty demand；
2) constraint is interacted with power grid
In formula:Power is interacted with power grid for t moment system,For maximum interaction power；
3) electricity of system, heat, cold reliability constraint:
In formula: β is confidence level, is set as 0.95.
8. the business garden integrated energy system Optimization Scheduling according to claim 1 based on chance constrained programming,
It is characterized by: handling constraints condition of opportunity by stochastic simulation technology, improved immune genetic algorithm solution is recycled to be based on
The business garden integrated energy system Optimal Operation Model of chance constrained programming；
For one group of given decision variable, stochastic simulation is for examining whether constraints condition of opportunity meets: for moment t, first
Set counter N '=0；Then random quantity is generated using Latin Hypercube simulationAnd become with decision
Amount substitutes into the left side of reliability constraint inequality together, if inequality is set up, N '=N '+1 is so repeated M times；If M is sufficient
Enough big, according to law of great number, when N '/M >=β, reliability constraint is set up, and one group of decision variable at this time is feasible
Solution, then selects optimal solution in feasible solution.
9. the business garden integrated energy system Optimization Scheduling according to claim 8 based on chance constrained programming,
It is characterized by: solving the optimal solution of above-mentioned Optimal Operation Model, the immune vaccine using improved immune vaccine algorithm
The process of algorithm is as follows:
(1) analysis optimization target and and its constraint equation, determine suitable coding form, herein use real coding；
(2) meeting unit output constraint and trading under constraint condition with power grid, N number of antibody is randomly generated and is mentioned from data base
M antibody is taken to constitute initial population；
(3) the expectation breeding potential of population antibody is evaluated, compared to the fitness evaluation of traditional immunization genetic algorithm, the index
Not only the antibody of fitness high (scheduling cost is small) had been encouraged, but also has inhibited the antibody of concentration high (similarity is high), it is ensured that antibody is more
Sample；
(4) population is subjected to descending sort by desired breeding potential, the antibody for taking top n outstanding constitutes parent group, while preceding m
Elite antibody is stored in data base；
(5) judge whether to meet termination condition, be, terminate；Otherwise carry out next step operation；
(6) based on step (4) result antibody is selected, is intersected, mutation operation, the group after being made a variation；
(7) evaluation of average expectation breeding potential is carried out to the population after variation behaviour, population point is carried out to the population that preset value is not achieved
Operation is cut, antibody is ranked up by desired breeding potential size, the antibody lower than population average expectation breeding potential is carried out again
Antibody after making a variation again and the antibody for being not carried out again mutation operation are merged into new population by mutation operation；
(8) turn to go to execute step (3), until output result.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160172854A1 (en) * | 2013-08-13 | 2016-06-16 | Accenture Global Services Limited | System for integrated multi-energy scheduling control in a micro-grid |
| CN107918811A (en) * | 2017-11-30 | 2018-04-17 | 国网天津市电力公司 | A kind of energy internet construction method for improving efficiency of energy utilization |
| CN108537409A (en) * | 2018-03-15 | 2018-09-14 | 广东电网有限责任公司电网规划研究中心 | A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic |
| WO2018196456A1 (en) * | 2017-04-28 | 2018-11-01 | 东南大学 | Modeling method for heat and electricity combined optimized scheduling model |
| CN109002941A (en) * | 2018-09-28 | 2018-12-14 | 南方电网科学研究院有限责任公司 | Consider integrated energy system lectotype selection and the method for planning capacity of heat accumulation link |
| CN109063992A (en) * | 2018-07-18 | 2018-12-21 | 国网重庆市电力公司经济技术研究院 | Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation |
| CN109658005A (en) * | 2018-12-29 | 2019-04-19 | 天津大学 | Consider garden integrated energy system lectotype selection and the method for planning capacity of electric energy storage |
| CN109784569A (en) * | 2019-01-23 | 2019-05-21 | 华北电力大学 | A kind of regional complex energy resource system optimal control method |
| CN109783909A (en) * | 2018-12-29 | 2019-05-21 | 天津大学 | Consider the comprehensive energy station equipment type selecting and method for planning capacity of a variety of energy storage devices |
| CN109885009A (en) * | 2019-03-19 | 2019-06-14 | 广东电网有限责任公司电网规划研究中心 | Meter and electricity turn the garden energy source optimization configuration method of providing multiple forms of energy to complement each other of gas planning |
-
2019
- 2019-08-07 CN CN201910723934.0A patent/CN110533225B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160172854A1 (en) * | 2013-08-13 | 2016-06-16 | Accenture Global Services Limited | System for integrated multi-energy scheduling control in a micro-grid |
| WO2018196456A1 (en) * | 2017-04-28 | 2018-11-01 | 东南大学 | Modeling method for heat and electricity combined optimized scheduling model |
| CN107918811A (en) * | 2017-11-30 | 2018-04-17 | 国网天津市电力公司 | A kind of energy internet construction method for improving efficiency of energy utilization |
| CN108537409A (en) * | 2018-03-15 | 2018-09-14 | 广东电网有限责任公司电网规划研究中心 | A kind of industrial park power distribution network collaborative planning method considering multiple-energy-source coupled characteristic |
| CN109063992A (en) * | 2018-07-18 | 2018-12-21 | 国网重庆市电力公司经济技术研究院 | Consider the power distribution network Expansion Planning method of regional complex energy resource system optimization operation |
| CN109002941A (en) * | 2018-09-28 | 2018-12-14 | 南方电网科学研究院有限责任公司 | Consider integrated energy system lectotype selection and the method for planning capacity of heat accumulation link |
| CN109658005A (en) * | 2018-12-29 | 2019-04-19 | 天津大学 | Consider garden integrated energy system lectotype selection and the method for planning capacity of electric energy storage |
| CN109783909A (en) * | 2018-12-29 | 2019-05-21 | 天津大学 | Consider the comprehensive energy station equipment type selecting and method for planning capacity of a variety of energy storage devices |
| CN109784569A (en) * | 2019-01-23 | 2019-05-21 | 华北电力大学 | A kind of regional complex energy resource system optimal control method |
| CN109885009A (en) * | 2019-03-19 | 2019-06-14 | 广东电网有限责任公司电网规划研究中心 | Meter and electricity turn the garden energy source optimization configuration method of providing multiple forms of energy to complement each other of gas planning |
Non-Patent Citations (4)
| Title |
|---|
| 曾鸣等: "综合能源系统建模及效益评价体系综述与展望", 《电网技术》 * |
| 曾鸣等: "综合能源系统建模及效益评价体系综述与展望", 《电网技术》, no. 06, 11 April 2018 (2018-04-11) * |
| 谭忠富等: "多能互补系统关键技术综述", 《分布式能源》 * |
| 谭忠富等: "多能互补系统关键技术综述", 《分布式能源》, no. 05, 15 October 2017 (2017-10-15) * |
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|---|---|---|---|---|
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| CN111191835A (en) * | 2019-12-27 | 2020-05-22 | 国网辽宁省电力有限公司阜新供电公司 | IES incomplete data load prediction method and system based on C-GAN transfer learning |
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