CN110110904A - Consider the integrated energy system optimization method of economy, independence and carbon emission - Google Patents
Consider the integrated energy system optimization method of economy, independence and carbon emission Download PDFInfo
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Abstract
The invention discloses the integrated energy system optimization methods for considering economy, independence and carbon emission, comprising the following steps: S1, establish integrated energy system, integrated energy system include power supply subsystem, for thermal sub-system, for refrigeration subsystem；S2, the electric load of power supply subsystem, the thermic load for thermal sub-system, the refrigeration duty for refrigeration subsystem are calculated respectively；S3, integrated energy system Model for Multi-Objective Optimization is established, integrated energy system Model for Multi-Objective Optimization includes optimization object, objective function and constraint condition；S4, integrated energy system Model for Multi-Objective Optimization is solved by non-dominated sorted genetic algorithm, obtains optimal performance scheme.The invention enables the overall performances of integrated energy system to reach most preferably, provides for the programmed decision-making of integrated energy system and instructs work.
Description
Technical field
The present invention relates to ENERGY PLANNING fields, more particularly to consider the comprehensive energy system of economy, independence and carbon emission
System optimization method.
Background technique
Clean the important topic that reliable energy supply is social development.From the point of view of cost and using energy source, use
Demand of the family side to Regional Energy supply system is strongly.Integrated energy system (IES) is the new of region energy mix system
Concept, target are the various energy resources demands by meeting user using the existing energy, this is also to realize clean energy resource supply
Effective way, the target that the development and realization that can promote renewable energy reduce environmental pollution.It is supplied with traditional single energy
Answer mode to compare, IES includes multiple subsystems such as hot, cold, electric, generated electricity by integrated renewable energy (RE), energy storage and other
Energy source electric generating device can satisfy a variety of workload demands.Therefore, the participation of a plurality of types of source generating units is so that IES
It is designed to an extremely complex problem.
Renewable energy (such as solar energy and wind energy) can solve climate warming, the energy safety of supply and high-energy source at
It plays a significant role in terms of this problem.Energy storage device can be put down by the electric energy that peak load shifting and storage of renewable energy generate
Weighing apparatus energy demand.They are the promising technologies for developing energy resource system.Energy storage device, which plays, accommodates more renewable energy
Role.However, many existing integrated energy planning models do not account for renewable energy or energy storage；Also, seldom
There are the relationship and operation reserve in research concern integrated energy system different sub-systems between unit, most of work are all bases
It is carried out in certain fixed hypothesis and boundary.Therefore, the configuring problem of integrated energy system is well studied far away,
New integrated energy system is especially from the beginning designed, how to plan the multiple-energy-source system combined with renewable energy and energy storage
How the optimum structure of system, select the type and capacity of candidate device, and what operation strategy should be taken total to minimize
Operating cost, these are still an open question in integrated energy system planning.
Summary of the invention
Object of the present invention is in view of the above-mentioned problems, providing a kind of using renewable energy and in conjunction with the synthesis of energy storage device
Energy resource system optimization method.
To achieve the goals above, the technical scheme is that
Consider the integrated energy system optimization method of economy, independence and carbon emission, comprising the following steps:
S1, establish integrated energy system, integrated energy system include power supply subsystem, for thermal sub-system, for refrigeration subsystem；
S2, the electric load of power supply subsystem, the thermic load for thermal sub-system, the refrigeration duty for refrigeration subsystem are carried out respectively
It calculates；
S3, integrated energy system Model for Multi-Objective Optimization is established, integrated energy system Model for Multi-Objective Optimization includes optimization
Object, objective function and constraint condition；
S4, integrated energy system Model for Multi-Objective Optimization is solved by non-dominated sorted genetic algorithm, is obtained most
Excellent performance scheme.
Further, subsystem of powering in the step S1 includes blower, photovoltaic panel, energy storage device and main power grid；It is described
It include jet dynamic control, gas fired-boiler, heat accumulation equipment for thermal sub-system；It is described for refrigeration subsystem include Absorption Refrigerator,
Electric refrigeration equipment.
Further, the energy storage device is battery, and the heat accumulation equipment is heat storage can, and the electricity refrigeration equipment is electricity system
Cold.
Further, the charge and discharge period calculation formula of the energy storage device are as follows:
Δ P=PE, load(t)+PIES, load(t)-PWT(t)-PPV(t)-PPgu, e(t)
Further, the electric load of power supply subsystem includes in user power consumption and integrated energy system in the step S2
The electricity consumption of electrical equipment, its calculation formula is:
PE, load(t)+PIES, load(t)=PWT(t)+PPV(t)+PBESS(t)+PPgu, e(t)+Pgrid(t)；
The calculation formula of the thermic load for thermal sub-system are as follows:
QT, load(t)=QPgu, h(t)+Qhst(t)+Qgb(t)；
The calculation formula of the refrigeration duty for refrigeration subsystem are as follows:
QC, load(t)=QPgu, c(t)+Qec(t)。
Further, optimization object includes blower, photovoltaic panel, the quantity of battery and gas electricity generator in the step S3
Group, heat storage can, gas fired-boiler, Absorption Refrigerator, electric refrigerating machine capacity；Its formula are as follows:
X=(NPV, NWT, Nbat, Cpgu, CHST, CGB, CAC, CEC)
Wherein NPV、NWT、NbatIt is the quantity of blower, photovoltaic panel and battery respectively；Cpgu、CHST、CGB、 CAC、CECIt is respectively
Jet dynamic control, heat storage can, gas fired-boiler, absorption cooling-water machine and electric refrigerating machine capacity.
Further, objective function includes annual total cost, external tariffs on electricity, carbon emission value in the step S3；It is public
Formula are as follows:
F=min (ATC, EER, CEV)；
Wherein, ATC is annual total cost, and EER is external tariffs on electricity, and CEV is carbon emission value.
Further, the annual total cost includes initial cost, operation expense, the replacement cost and energy cost,
Its calculation formula is:
ATC=CIC+CO&M+CRE+CE
CIC, j=Cj·CU, j=Nj·CU, j；
Wherein CIC, jIt is the initial outlay cost of equipment in integrated energy system；M is the quantity of equipment；The multiple benefit system of R capital
Number；I is interest rate；N is the life cycle management of integrated energy system；CjThe capacity of equipment j；NjIt is the quantity of equipment j；CU, jIt is equipment
The unit cost of j；
The calculation formula of the external electricity charge are as follows:
Wherein, EGrid, buyFor main power grid purchase of electricity, EloadFor user power consumption, EIESFor equipment in integrated energy system
Electricity consumption；
The carbon emission amount derives from the burning of fuel and the discharge of main grid generation, its calculation formula is:
Wherein, μC, gIt is the carbon emission transformation ratio of natural gas；μC, eIt is the carbon emission transformation ratio for buying main power grid electric.
Further, constraint condition includes load balance constraint, region area constraint in the step S3；Load balance is about
The calculation formula of beam are as follows:
PE, load(t)+PIES, load(t)=PIES, output
QT, load(t)=QPgu, h(t)+Qhst(t)+Qgb(t)
QC, load(t)=QPgu, c(t)+Qec(t)；
The calculation formula of the region area constraint are as follows:
Wherein, NWT、NPVIt is the sum of blower and photovoltaic panel, A respectivelyPV、APVIt is total available face of blower and photovoltaic panel respectively
Product, AU, WT、AU, PVIt is the unit area of blower and photovoltaic panel panel.
Compared with prior art, the advantages and positive effects of the present invention are:
The present invention pass through integrated energy system different sub-systems between operation reserve, establish including wind energy conversion system, photovoltaic panel,
The comprehensive energy system of the main power grid connection such as energy-storage system of accumulator, cooling heating and power generation system, regenerative furnace, gas fired-boiler, electric cooler
System model, and the present invention is built by comprehensively considering total annual cost (ATC), external electricity ratio (EER) and carbon emission value (CEV)
Vertical Model for Multi-Objective Optimization, obtains the input data including load and environmental condition from management organization, is based on NSGA-II
Method, and optimum results have been obtained by Topsis method according to the three above indexs, make the overall performance of integrated energy system
Reach best, provides guidance for the programmed decision-making of integrated energy system.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is optimized flow chart of the invention；
Fig. 2 is integrated energy system structure chart；
Fig. 3 is integrated energy system operation reserve figure；
Fig. 4 is non-dominated sorted genetic algorithm flow chart；
Fig. 5 is the resources supplIes schematic diagram of integrated energy system；
Fig. 6 is Pareto optimality sequence diagram；
Fig. 7 is top view, right view and the left view of Pareto optimality sequence；
Fig. 8 is daily load schematic diagram；
Fig. 9 is the supplied for electronic system output power schematic diagram of different allocation plans；
Figure 10 is different allocation plans for thermal sub-system output power schematic diagram；
Figure 11 is different allocation plans for refrigeration subsystem output power schematic diagram.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment, any modification, equivalent replacement, improvement and so on should all be included in the protection scope of the present invention.
As shown in Fig. 1 and Figure 11, the purpose of the invention is to obtain the optimal capacity of each unit in IES, method such as Fig. 1
It is shown.First according to blower (WT), photovoltaic panel solar panel (PV), battery (BESS), regenerative apparatus (HST), cogeneration
The characteristics of device (CCHP) and refrigeration equipment, carries out system configuration, and the operation reserve based on configuration is discussed.Secondly, establishing
The capacity Optimized model for considering multiple target, the data of load and resources supplIes (wind speed, radiation intensity) are input in model, benefit
Pareto optimality disaggregation is obtained with NSGA-II.Finally, in the design scheme of Pareto disaggregation, most using the selection of Topsis method
Excellent scheme obtains ideal scheme and non-ideal scheme by multiple target criterion, and selects optimal side according to distance between the two
Case.
Conventional IES system is made of electricity, heat, refrigeration subsystem.The present invention builds typical integrated energy system
Mould, and the structure chart of IES is given, as shown in Figure 2.Within the system, blower, photovoltaic panel and energy-storage system and main power grid are integrated
Form power supply subsystem.In addition, including jet dynamic control (PGU), gas fired-boiler (GB) and heat accumulation equipment for thermal sub-system
(HST).It is made of for refrigeration subsystem Absorption Refrigerator (AC) and electric refrigeration equipment (EC).Jet dynamic control (PGU) is recognized
To be to provide the subsystem of electric energy and thermal energy under electricity determining by heat mode.Gas fired-boiler, main power grid, electric refrigerating machine are respectively as confession
Warm, power supply and refrigeration are used as peak regulation equipment.
For the energy exchange between different sub-systems, due to the operation reserve of electricity determining by heat, combustion gas in subsystem of powering
The output power of generating set (PGU) is influenced by heating system, will be from power supply subsystem for the electric refrigeration equipment in refrigeration subsystem
System obtains electric energy.
Obviously, under different operation strategies, it will lead to the different system design scheme of IES.In addition, operation plan appropriate
Slightly there is important influence to the performance of system.In this part, the operation reserve of integrated energy system is described.Fig. 3 gives
The running optimizatin policy map of IES is gone out, it is intended to meet the needs of hot and cold and electric.
For energy balance, each subsystem provides energy to meet workload demand.System in the present invention be it is grid-connected,
This means that being using electricity as core.Electrical energy demands include demand (the i.e. electric energy of electric refrigerating machine of user demand and IES component
Demand).More specifically, electric energy is mainly provided by blower, photovoltaic panel and cogeneration in the present invention, and remaining electricity then stores
In BESS, when SOC reaches maximum value, remaining electricity will be sold to main power grid.On the other hand, it is not able to satisfy in blower and photovoltaic panel
When electricity needs, the least limit that battery will discharge until SOC, and when all jet dynamic controls are not able to satisfy load, it lacks
Electricity will be bought from main power grid.
PE, load(t)+PIES, load(t)=PWT(t)+PPV(t)+PBESS(t)+PPgu, e(t)+Pgrid(t) (1)
In addition, thermic load and refrigeration duty are mainly provided by cogeneration.When PGU work in electricity determining by heat mode, storage
When hot tank underfill, output power not only can satisfy thermic load and refrigeration duty requirement, but also can be by hot water storage to heat accumulation
In tank.When heat storage can is full of, PGU contributes according to actual heating load and refrigeration duty.When co-generation unit output power deficiency,
Heat storage can be with supplemental output.However, if the output of the two is not still able to satisfy energy demand, gas fired-boiler and electricity
Freeze and will meet the energy of missing as peak adjusting device.
QT, load(t)=QPgu, h(t)+Qhst(t)+Qgb(t) (2)
QC, load(t)=QPgu, c(t)+Qec(t) (3)
For the operation reserve of main component in IES, the output power of blower and photovoltaic panel is determined by wind speed and radiation intensity
Fixed, electric energy storage device is used for the storage after meeting loading demand and comes from blower, the excess energy of photovoltaic panel and cogeneration.Phase
Instead, when jet dynamic control deficiency, electric energy storage device electric discharge, the charging and discharging period be can be described as:
Δ P=PE, load(t)+PIES, load(t)-PWT(t)-PPV(t)-PPgu, e(t) (4)
In addition, the charge and discharge of battery should also follow the limitation of exchange power and SOC.When exchange power exceeds the limitation of SOC
When, main power grid will be by buying electricity and selling electric participation system.In addition, the operation reserve of heat storage can is similar with energy-storage system.It is stored
Thermal energy from CCHP, in heat supply deficiency, heat storage can be it is exothermic, when thermal energy surplus, heat storage can is filled with extra energy
Amount.Finally, for peak regulation equipment, when heating unit and refrigeration unit are not able to satisfy energy requirement, gas fired-boiler and electricity refrigeration
Machine is mainly used for peak period.
The design optimization of integrated energy system is a complicated problem, it is directed not only to the operation plan of each subsystem
Slightly, the performance of system is further related to.In order to obtain the allocation optimum of each unit in IES, system economy, independence are being considered
On the basis of carbon emission, Model for Multi-Objective Optimization is established.And it is realized by optimization method including optimization object, target letter
The construction of number and the Model for Multi-Objective Optimization of constraint.Finally, Scheme Choice is carried out, according to different requirement of system design, in pa
Optimal design is selected in the result of tired support optimal solution set.
Integrated energy system optimization object includes blower, photovoltaic panel, battery, PGU, heat storage can, gas fired-boiler, absorption system
The capacity of cold and electric refrigerating machine, wherein the capacity of blower, photovoltaic panel and battery is indicated with separate unit number.It can be summarized as following
Formula 6.It is furthermore assumed that the electricity needs and resources supplIes in systems life cycle are constant.
X=(NPV, NWT, Nbat, Cpgu, CHST, CGB, CAC, CEC) (6)
Wherein NPV, NWT, NbatIt is blower, the quantity of photovoltaic panel and battery；Cpgu, CHST, CGB, CAC, CECIt is gas electricity generator
Group, heat storage can, gas fired-boiler, the capacity of absorption chiller and electric refrigeration equipment group.
The optimal volume solutions of IES for comprehensively considering economy, independence and carbon emission in order to obtain, establish based on three mesh
The Model for Multi-Objective Optimization of scalar functions.Optimization process is provided with annual total cost (ATC), external tariffs on electricity (EER) and carbon emission
It is worth (CEV) three optimizing index.The economy optimization of IES can be reflected by the objective function of ATC minimum value.The present invention
Calculate the life cycle cost of IES, including initial outlay, operation expense, the replacement cost and energy cost.Initial outlay
It is mainly reflected in the purchase cost of IES equipment.Operation and maintenance cost is mainly the cost of equipment in the process of running.It is reset to
Originally the alternative costs at the end of equipment life are primarily referred to as.Finally, the energy cost of IES represent fuel cost and with main power grid
Interaction cost.In addition, the objective function of EER minimum value can reflect the independence optimization of IES, show that major network purchase of electricity Zhan is total
The ratio of electricity consumption, EER value is smaller, and the independence of IES is stronger.Finally, using the minimum value of CEV as objective function, the present invention is ground
The main source for the IES carbon emission studied carefully is the burning of natural gas and the main power grid of coal power generation.Multiple objective function is as follows:
F=min (ATC, EER, CEV) (7)
1, objective function I
Annual total cost is used to reflect the economy of total life cycle.It includes initial cost, operation expense, the replacement cost
And energy cost, as shown by the equation:
ATC=CIC+CO&M+CRE+CE (8)
CIC, j=Cj·CU, j=Nj·CU, j (11)
Wherein CIC, jIt is the initial outlay cost of equipment；M is the quantity of equipment；R capital compound interest factor；I is interest rate；N is
The life cycle management of integrated energy system；CjThe capacity of equipment j；NjIt is the quantity of equipment j, is mainly used for blower in the present invention
Photovoltaic panel and energy storage；CU, jIt is the unit cost of equipment j.
Wherein, CO&M, jIt is the operation expense of equipment j；cO&m, u, jIt is the unit operation expense of equipment j；F is logical
Goods expansion rate；NjThe quantity of replacing apparatus；CfuelThe fuel cost of gas consumption；CgridIt is the cost that electricity is bought from power grid；cgas
Gas Prices；cgridIt is electricity price；VpguIt is the natural gas of jet dynamic control consumption；VgbThe natural gas of gas fired-boiler consumption
2, objective function II
The independence of system is described with external electricity ratio, EER value is smaller, and IES independence is stronger.
External electricity ratio determines by major network purchase of electricity, wherein EGrid, buyFor main online shopping electricity, EloadFor user power consumption,
EIESFor IES electricity consumption (being EC electricity consumption in the present invention).
3, objective function III
Carbon emission is the important indicator of environmental benefit.The carbon emission that the system generates is mainly derived from burning and the master of fuel
The discharge of grid generation.
Wherein μC, gIt is the carbon emission transformation ratio of natural gas, g/kWh；μC, eThe carbon emission for carrying out the electric power of autonomous power grid turns
Change coefficient, g/kWh.The parameter of carbon emission is listed in table 2.
1. carbon emission parameter of table
1, load balance constrains
For load balance, each subsystem provides energy to meet energy requirement.System in the present invention be it is grid-connected,
This means that being using electric energy as core.Electrical energy demands include the demand of user and demand (the i.e. electricity of electric refrigerating machine of IES component
Energy demand).
PE, load(t)+PIES, load(t)=PIES, output (17)
QT, load(t)=QPgu, h(t)+Qhst(t)+Qgb(t) (18)
QC, load(t)=QPgu, c(t)+Qec(t) (19)
2, region area limits
The construction of IES need to meet region area constraint, region area constraint can by generator unit number (i.e. blower,
Photovoltaic panel) it reflects.
Wherein, NWT、NPVIt is the sum of blower and photovoltaic panel, APV、APVIt is total usable area of blower and photovoltaic panel respectively,
AU, WT、AU, PVIt is the unit area of wind turbine and photovoltaic panel panel.
In addition, genetic algorithm (GA) is that the widely used meta-heuristic optimization based on natural selection is calculated in roulette strategy
Method.The basic step of genetic algorithm has initial population, selection, intersection and mutation.2002, non-dominated sorted genetic algorithm II
(NSGA-II) it is suggested.It can ensure that outstanding individual will not be dropped during evolution, to improve optimum results
Accuracy.In addition, NSGA-II is a kind of fast algorithm, it is widely used to multi-objective problem.Therefore, NSGA-II is applicable in
In the multiple target configuration optimization for solving the problems, such as IES.NSGA-II integrated energy system algorithm flow chart is as shown in Figure 4.
Below for there are the residential quarters of electricity, hot, cold energy demand, integrated energy system configuration optimization model is answered
For case study.Time interval in the present invention is 1h, that is, assumes that the data of a hour are all fixed values, have in 1 year
8760 points.For the details of present case, it can be used for the area of photovoltaic panel installation close to 1700 square metres, can be used for assembling
Area close to 400 square metres.Therefore, because technical parameter shown in document and document, the maximum of photovoltaic panel panel and blower
Installation number is 1000 and 30.Finally, obtaining the energy such as electric power, cooling supply, the heating of the residential quarter by local meteorological department
The historical data of demand and wind speed and radiation intensity.
The renewable energy condition such as wind speed, radiation intensity be China Meteorological Administration provide important input data, influence blower,
The output power of photovoltaic panel.In December, 2015 in November, 2016, the radiance and wind speed of this community are as shown in Figure 5.It is another
Aspect, the energy demand of this community include heat supply, cooling supply and electric energy.
The season of the community is spring for March to May, and summer is June to August, and autumn is September to November, winter 12
The moon was to 2 months.In addition, the refrigeration energy of this community is only needed in summer, and it is annual have electric energy and a thermal demand, and this society
The thermal demand in area is hot water, therefore its value in summer is in extremely low level.
IES system lifetim is set as 20 years in the present invention, and is connected with main power grid.According to real data, electric power and day
Right gas price lattice are set to fixed value, as shown in table 2
2. project economic parameter of table
The present invention optimizes the capacity of photovoltaic panel, wind turbine and battery number of units and other equipment.Cost variations include just
In the service life of every equipment, be shown in Table 3 in beginning cost, operation and maintenance (O&M) cost and IES.
The unit cost of each component of table 3. and service life
The parameter setting used in optimization process is as follows.Population Size is set as 200, number of iterations 100.According to available area
The limitation of blower and photovoltaic panel is set 0~300 and 0~1000 by domain.
By setting primary data and parameter, capacity of the invention is realized by NSGA-II in MATLAB 2014a
Optimized model.Based on three objective functions (formula 8-10) obtain Pareto optimality disaggregation as a result, population size is set as
200, all 60 points are all based on the feasible solution of three objective functions in Pareto optimality disaggregation, as shown in Figure 6；
Present the left view, right view and top view of Fig. 6 respectively in Fig. 7 (a), (b), (c).It can from optimum results
Can be clearly seen, there is connection between three indexs of objective function.The increase of ATC will lead to the decline of EER and CEV, and
EER changes compared with CEV to be become apparent.In addition, the relationship of EER and CEV is increasingly complex, after CEV first reduces with the increase of EER
Increase.This is because main power grid power supply can reduce the yield of cogeneration of heat and power to a certain extent.It may be concluded that higher system
Independence of uniting and lower carbon emission need more investments.
Therefore, it in multi-objective optimization question, needs to concentrate to obtain from Pareto optimal solution according to different indexs optimal
Solution.In the present invention, three indexs of Optimized model are ATC, EER and CEV.Due to each solution of Pareto optimal solution set
Scheme is all the feasible program of IES, therefore feasible program selection course is for determining that the preferred plan of IES component is necessary.
Researcher has developed a large amount of Scheme Choice method, and Topsis method is widely used according to ideal solution and suboptimal solution
The distance between select optimal solution.Ideal solution and undesirable solution are calculated using Pareto optimality sequence and index preference.This hair
The bright weight by each evaluation index is set as equal, and three indexs follow 1/3 hypothesis.Table 4 gives based on Topsis method
Pareto optimal solution set ideal solution and undesirable solution, table 5 give based on three objective functions selection optimal solution, table 6
Give the allocation optimum number of each component.
Standardization result of the table 4. based on Topsis method
Solution as shown in Table 4 as a result, having obtained the optimal solution based on the equal preference of three objective functions.In addition to obtaining base
In outside the single-goal function of Pareto optimal sequence, on the basis of ATC drives solution, the total annual cost of Pareto optimal sequence
Minimum reduces 89.8% than optimal solution, but system independence and the performance of reduction pollution are worst.In addition, EER drive scheme has
There is best system independence, but ATC and CEV are higher than optimal case by 43.79% and 19.57% respectively.It is driven finally, for CEV
Dynamic solution, the carbon emission amount that it is generated is minimum, sacrifices the economy and independence of system, ATC and EER are respectively than most
Excellent scheme high 31.67% and 79.27%.From the above analysis, optimal case has good economy, independence and carbon
Emission performance.
Optimum results of the table 5. based on Topsis method
The optimum capacity of each component of table 6.
In addition, the optimal number of each equipment and capacity shown in table 6 are set to the maximum limit, because blower is clear
Clean jet dynamic control has better efficiency compared with photovoltaic panel.In the solution of ATC driving, the quantity of photovoltaic panel
Seldom, it also can reflect out poor economic situation.Number of batteries in best solution is 70, it is contemplated that expensive throwing
Cost is provided, this is a relatively large quantity, this can also be reflected by what ATC drove without battery solution,
The battery of large capacity is inevitably needed using renewable energy.Moreover, the capacity of jet dynamic control and the capacity of boiler
It is inversely proportional.In ATC drive scheme, boiler capacity is larger, and gas electricity generator pool-size is smaller, but in view of carbon pollution and solely
Vertical property, excess-three kind scheme do not have boiler capacity.Meanwhile the capacity of Absorption Refrigerator and electric cold type refrigeration machine also phase
Instead, under EER driving, the capacity of EC is minimum.Finally, the capacity of heat storage can is maximum, more by providing under EER driving
Alternating current reduces electricity needs.
In order to verify the performance of above-mentioned volume solutions, a typical summer with electricity, hot and cold load is had chosen, is used
Optimal volume solutions analyze the performance of IES equipment.IES equipment can also be verified whether according to fortune proposed by the present invention
Row strategy works verify the credibility of Optimized model proposed by the present invention.
Optimal planning, ATC driving plan, EER driving plan and the ability of CEV driving plan are all surveyed on the day of
Examination, as shown in figure 8, the equipment output power of electricity, hot and cold subsystem is as shown in Fig. 9,10,11.It indicates to be based on three in the upper left corner
The performance of the optimal solution of a objective function, the and the case where upper right corner indicates capacity plan drive based on ATC, EER driving with
The assembly operating state of CEV driving is respectively displayed on the lower left corner and the lower right corner.
Fig. 9 describes operating condition of the power subsystem under four kinds of different capabilities schemes.The difference of objective function preference
Result in the difference of capacity plan.As shown in Figure 9, the difference of component capacity results in the difference of running environment.Major network is kept
The power-balance of system, it is contemplated that the independence and carbon emission of system, in optimal plan, EER driving plan and CEV driving meter
There is no energy input in drawing.
Fig. 9 shows the operating status of power subsystem in the works in four kinds of different capabilities.Invention describes IES electricity
Blower, photovoltaic panel, CCHP, energy-storage system of accumulator and major network in gas subsystem.The difference of objective function preference results in appearance
The difference that meter is drawn.As shown in Figure 9, the difference of element volume results in different operating condition.For the device, at four
In volume solutions, the number of blower is all 30, therefore the output power of blower is identical.In addition, the output work of photovoltaic panel
Rate additionally depends on the quantity of photovoltaic panel, drives in the works in ATC, and the quantity of photovoltaic panel is seldom, causes photovoltaic panel output weaker.This
Outside, the power output power of CCHP is the work under nominal power of jet dynamic control whole day at this time based on thermic load
Make, therefore the generated energy per hour of CCHP is identical, but in the solution of ATC driving, the capacity of jet dynamic control
Smaller, the output power of CCHP is the smallest in four kinds of schemes in electronic system.Battery designed by the present invention be for
Store the extra electric energy that photovoltaic panel, WT and CCHP are generated.In the solution of EER driving, the capacity of battery is maximum, leads
Cause BESS that there is biggish charge power.On the contrary, big due to investing, the quantity of battery is in the solution of ATC driving
Zero, without battery charging and discharging.In view of the independence and carbon emission of system, the scheme of the optimal of system, EER and CEV driving is all
Electric behavior is not bought, the case where major network a large amount of power purchases forms distinct contrast in the scheme of this driving with ATC, and here it is EER
Good balanced action is played in IES system with CEV, while in view of the carbon emission of the independence of system and system, being
System is not needed to power grid power purchase.
Then for the output sequence of equipment, operation reserve is determined are as follows: renewable jet dynamic control is run based on environment,
Jet dynamic control is worked based on thermic load, and dump energy first stores in the battery, and battery Chu Manhou again sells dump energy
To power grid, the operation order of renewable jet dynamic control are as follows: renewable jet dynamic control is run based on environment, PGU
It is worked based on thermic load, dump energy is firstly stored in battery, and after battery is full, dump energy is sold to power grid.?
In Optimized model, the operation reserve operational excellence of electrical subsystem.
It for thermal sub-system include supply of cooling, heating and electrical powers, gas fired-boiler and heat storage can in addition, of the invention.In Figure 10, show
The operating status of heating system in four kinds of capacity solutions.Identical with the distribution sequence of Fig. 9, it is clearly reflected not
With the variation of capacity plan.
For the capacity of equipment, CCHP is the main energy sources supply of system, runs its today, comes under nominal power
It is a fixed value hourly from the thermal power of jet dynamic control.In ATC drive scheme, the capacity of jet dynamic control
Minimum, in CEV drive scheme, gas electricity generator pool-size is maximum, and compared with gas fired-boiler, CCHP has better emission reduction
Can, but invest bigger.Then using the thermal energy of heat storage can storage cogeneration, and the situation insufficient in cogeneration energy supply
Lower output thermal energy.The power of heat accumulation is influenced by jet dynamic control power, and jet dynamic control power is bigger, needs to store
Energy is more.But under the volume solutions of ATC driving, the output power very little of jet dynamic control, it is therefore desirable to which heat accumulation is to being
System energy supply.For the boiler in system, it is a kind of cheap equipment, but its carbon emission value is very high, can only be in ATC
It is configured under the solution of driving.In addition, jet dynamic control is as main warm for the operation reserve of heat supply subscriber device
Source, when jet dynamic control deficiency, heat accumulation is run to meet heat demand, and gas fired-boiler is finally run to meet heat supply need
It asks.
Finally, Figure 11 shows the performance for refrigeration subsystem in four optimizing capacity solutions.In for refrigeration subsystem, absorb
Formula refrigeration machine is main jet dynamic control, and when output power deficiency then using electricity refrigeration.
Claims (9)
1. considering the integrated energy system optimization method of economy, independence and carbon emission, it is characterised in that: including following step
It is rapid:
S1, establish integrated energy system, integrated energy system include power supply subsystem, for thermal sub-system, for refrigeration subsystem；
S2, respectively to the power supply electric load of subsystem, the thermic load for thermal sub-system, the balance of the refrigeration duty for refrigeration subsystem into
Row calculates；
S3, establish integrated energy system Model for Multi-Objective Optimization, integrated energy system Model for Multi-Objective Optimization include optimization object,
Objective function and constraint condition；
S4, integrated energy system Model for Multi-Objective Optimization is solved by non-dominated sorted genetic algorithm, obtains optimality
It can scheme.
2. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as described in claim 1
Be: subsystem of powering in the step S1 includes blower, photovoltaic panel, energy storage device and main power grid；It is described for thermal sub-system packet
Include jet dynamic control, gas fired-boiler, heat accumulation equipment；Described for refrigeration subsystem includes Absorption Refrigerator, electric refrigeration equipment.
3. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 2
Be: the energy storage device is battery, and the heat accumulation equipment is heat storage can, and the electricity refrigeration equipment is electric refrigerating machine.
4. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 3
It is: the charge and discharge period calculation formula of the energy storage device are as follows:
Δ P=PE, load(t)+PIES, load(t)-PWT(t)-PPV(t)-PPgu, e(t)
Wherein, Δ P is electric load difference, PE, loadIt (t) is t moment user power consumption, PIES, loadIt (t) is electricity consumption in t moment system
The power consumption of equipment, PWT(t)、PPV(t)、、PPgu, e(t) be t moment blower, photovoltaic, combustion engine electricity power output, PBESS(t) when being t
Carve the charge-discharge electric power of energy storage.
5. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 4
Be: the electric load for subsystem of powering in the step S2 includes the use of electrical equipment in user power consumption and integrated energy system
Electricity, electric equilibrium calculation formula are as follows:
PE, load(t)+PIES, load(t)=PWT(t)+PPV(t)+PBESS(t)+PPgu, e(t)+Pgrid(t)；
Wherein, Pgrid(t) be t moment and power grid interaction electricity；
The calculation formula of the heat load balance for thermal sub-system are as follows:
QT, load(t)=QPgu, h(t)+Qhst(t)+Qgb(t)；
Wherein, QT, loadIt (t) is refrigeration duty in t moment system, QPgu, hIt (t) is that the hot of t moment combustion engine is contributed, Qhst(t) when being t
Carve the heat power output of heat accumulation equipment, QgbIt (t) is that the hot of t moment gas fired-boiler is contributed；
The calculation formula of the refrigeration duty balance for refrigeration subsystem are as follows:
QC, load(t)=QPgu, c(t)+Qec(t)；
Wherein, QC, load(t) it is refrigeration duty in t moment system；QPgu, c(t) be t moment lithium bromide chiller cold power output；Qec(t)
It is the cold power output of t moment electrical chillers.
6. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 5
Be: optimization object includes blower, photovoltaic panel, the quantity of battery and jet dynamic control, heat storage can, combustion in the step S3
Gas boiler, Absorption Refrigerator, electric refrigerating machine capacity；Its formula are as follows:
X=(NPV, NWT, Nbat, Cpgu, CHST, CGB, CAC, CEC)
Wherein, NPV、NWT、NbatIt is the quantity of blower, photovoltaic panel and battery respectively；Cpgu、CHST、CGB、CAC、CECIt is combustion gas hair respectively
Motor group, heat storage can, gas fired-boiler, absorption cooling-water machine and electric refrigerating machine capacity.
7. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 6
Be: objective function includes annual total cost, external tariffs on electricity, carbon emission value in the step S3；Its formula are as follows:
F=min (ATC, EER, CEV)；
Wherein, ATC is annual total cost, and EER is external tariffs on electricity, and CEV is carbon emission value.
8. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 7
Be: the annual total cost includes initial cost, operation expense, the replacement cost and energy cost, its calculation formula is:
ATC=CIC+CO&M+CRE+CE
CIC, j=Cj·CU, j=Nj·CU, j；
Wherein CIC, jIt is the initial outlay cost of equipment in integrated energy system；M is the quantity of equipment；R capital compound interest factor；I is
Interest rate；N is the life cycle management of integrated energy system；CjThe capacity of equipment j；NjIt is the quantity of equipment j；CU, jIt is the list of equipment j
Position cost；
The calculation formula of the external electricity charge are as follows:
Wherein, EGrid, buyFor main power grid purchase of electricity, EloadFor user power consumption, EIESFor the electricity consumption of equipment in integrated energy system
Amount；
The carbon emission amount derives from the burning of fuel and the discharge of main grid generation, its calculation formula is:
Wherein, μC, gIt is the carbon emission transformation ratio of natural gas；μC, eIt is the carbon emission transformation ratio for buying main power grid electric.
9. considering the integrated energy system optimization method of economy, independence and carbon emission, feature as claimed in claim 8
Be: constraint condition includes load balance constraint, region area constraint in the step S3；The calculation formula of load balance constraint
Are as follows:
PE, load(t)+PIES, load(t)=PIES, output
QT, load(t)=QPgu, h(t)+Qhst(t)+Qgb(t)
QC, lodd(t)=QPgu, c(t)+Qec(t)；
The calculation formula of the region area constraint are as follows:
Wherein, NWT、NPVIt is the sum of blower and photovoltaic panel, A respectivelyPV、APVIt is total usable area of blower and photovoltaic panel respectively,
AU, WT、AU, PVIt is the unit area of blower and photovoltaic panel panel.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563616A (en) * | 2020-04-17 | 2020-08-21 | 许继集团有限公司 | Comprehensive energy system optimization method |
| CN111932028A (en) * | 2020-09-01 | 2020-11-13 | 大连理工大学 | Clean energy system capacity optimization method and system based on carbon natural circulation consumption |
| CN112001529A (en) * | 2020-08-03 | 2020-11-27 | 华北电力大学 | Configuration optimization method for electricity-cold-heat comprehensive energy system |
| CN112052430A (en) * | 2020-08-17 | 2020-12-08 | 重庆大学 | Photovoltaic station correlation comprehensive evaluation system based on improved TOPSIS |
| CN112465263A (en) * | 2020-12-11 | 2021-03-09 | 国网宁夏电力有限公司经济技术研究院 | Comprehensive energy operation optimization method suitable for multiple scenes |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130166043A1 (en) * | 2011-07-20 | 2013-06-27 | Nec Laboratories America, Inc. | Optimal energy management of a microgrid system using multi-objective optimization |
| CN106055773A (en) * | 2016-05-27 | 2016-10-26 | 东南大学 | Establishment method of configuration model of multi-regional comprehensive energy system in combination with thermal network model |
| CN107301275A (en) * | 2017-05-27 | 2017-10-27 | 上海电气分布式能源科技有限公司 | Configuration and capacity optimization method for distributed energy resource system Technology Selection |
| CN108646552A (en) * | 2018-04-16 | 2018-10-12 | 杭州电子科技大学信息工程学院 | The Multipurpose Optimal Method of Distribution of Natural formula energy unit parameter based on genetic algorithm |
| CN109299823A (en) * | 2018-09-22 | 2019-02-01 | 天津大学 | A kind of micro- energy net multi-objective planning method considering thermal method sea water desalination |
| CN109474025A (en) * | 2018-10-08 | 2019-03-15 | 国网能源研究院有限公司 | Optimized dispatching model of park level comprehensive energy system |
| CN109617142A (en) * | 2018-12-13 | 2019-04-12 | 燕山大学 | A kind of CCHP type micro-capacitance sensor Multiple Time Scales Optimization Scheduling and system |
-
2019
- 2019-04-17 CN CN201910307884.8A patent/CN110110904A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130166043A1 (en) * | 2011-07-20 | 2013-06-27 | Nec Laboratories America, Inc. | Optimal energy management of a microgrid system using multi-objective optimization |
| CN106055773A (en) * | 2016-05-27 | 2016-10-26 | 东南大学 | Establishment method of configuration model of multi-regional comprehensive energy system in combination with thermal network model |
| CN107301275A (en) * | 2017-05-27 | 2017-10-27 | 上海电气分布式能源科技有限公司 | Configuration and capacity optimization method for distributed energy resource system Technology Selection |
| CN108646552A (en) * | 2018-04-16 | 2018-10-12 | 杭州电子科技大学信息工程学院 | The Multipurpose Optimal Method of Distribution of Natural formula energy unit parameter based on genetic algorithm |
| CN109299823A (en) * | 2018-09-22 | 2019-02-01 | 天津大学 | A kind of micro- energy net multi-objective planning method considering thermal method sea water desalination |
| CN109474025A (en) * | 2018-10-08 | 2019-03-15 | 国网能源研究院有限公司 | Optimized dispatching model of park level comprehensive energy system |
| CN109617142A (en) * | 2018-12-13 | 2019-04-12 | 燕山大学 | A kind of CCHP type micro-capacitance sensor Multiple Time Scales Optimization Scheduling and system |
Non-Patent Citations (3)
| Title |
|---|
| 刘成运等: ""Ａ级数据中心综合能源***多目标优化设计和调度"", 《电力***自动化》 * |
| 戴光明，王茂才著: "《多目标优化算法及在卫星星座设计中的应用》", 30 November 2009, 中国地质大学出版社 * |
| 郭谆钦著: "《电火花加工技术及精度改善措施》", 30 June 2015, 电子科技大学出版社 * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111563616A (en) * | 2020-04-17 | 2020-08-21 | 许继集团有限公司 | Comprehensive energy system optimization method |
| CN111563616B (en) * | 2020-04-17 | 2023-11-28 | 许继集团有限公司 | Comprehensive energy system optimization method |
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| CN112052430A (en) * | 2020-08-17 | 2020-12-08 | 重庆大学 | Photovoltaic station correlation comprehensive evaluation system based on improved TOPSIS |
| CN111932028A (en) * | 2020-09-01 | 2020-11-13 | 大连理工大学 | Clean energy system capacity optimization method and system based on carbon natural circulation consumption |
| CN111932028B (en) * | 2020-09-01 | 2024-04-30 | 大连理工大学 | Clean energy system capacity optimization method and system based on natural carbon circulation digestion |
| CN112465263A (en) * | 2020-12-11 | 2021-03-09 | 国网宁夏电力有限公司经济技术研究院 | Comprehensive energy operation optimization method suitable for multiple scenes |
| CN113239529A (en) * | 2021-04-29 | 2021-08-10 | 国网江苏省电力有限公司苏州供电分公司 | Multi-objective optimization method of comprehensive energy system |
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