CN104123596B - Power supply optimization planning method considering renewable energy - Google Patents
Power supply optimization planning method considering renewable energy Download PDFInfo
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- CN104123596B CN104123596B CN201410370735.3A CN201410370735A CN104123596B CN 104123596 B CN104123596 B CN 104123596B CN 201410370735 A CN201410370735 A CN 201410370735A CN 104123596 B CN104123596 B CN 104123596B
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- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000005611 electricity Effects 0.000 claims abstract description 96
- 238000004364 calculation method Methods 0.000 claims abstract description 26
- 238000004519 manufacturing process Methods 0.000 claims abstract description 23
- 230000001172 regenerating effect Effects 0.000 claims description 49
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 33
- 239000000446 fuel Substances 0.000 claims description 29
- 238000009434 installation Methods 0.000 claims description 21
- 230000035699 permeability Effects 0.000 claims description 16
- 238000013028 emission testing Methods 0.000 claims description 13
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 12
- 239000003500 flue dust Substances 0.000 claims description 11
- 230000005540 biological transmission Effects 0.000 claims description 9
- 238000012423 maintenance Methods 0.000 claims description 9
- IFLVGRRVGPXYON-UHFFFAOYSA-N adci Chemical compound C12=CC=CC=C2C2(C(=O)N)C3=CC=CC=C3CC1N2 IFLVGRRVGPXYON-UHFFFAOYSA-N 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 8
- 230000007613 environmental effect Effects 0.000 claims description 7
- 238000004088 simulation Methods 0.000 claims description 7
- 238000013459 approach Methods 0.000 claims description 6
- 239000003345 natural gas Substances 0.000 claims description 6
- 239000003344 environmental pollutant Substances 0.000 claims description 5
- 231100000719 pollutant Toxicity 0.000 claims description 5
- GQPLMRYTRLFLPF-UHFFFAOYSA-N Nitrous Oxide Chemical class [O-][N+]#N GQPLMRYTRLFLPF-UHFFFAOYSA-N 0.000 claims description 4
- 238000011084 recovery Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000008520 organization Effects 0.000 claims description 3
- 238000010977 unit operation Methods 0.000 claims description 3
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention discloses a power supply optimization planning method considering renewable energy, which comprises the steps of collecting information to generate a power supply optimization planning basic database, wherein the power supply optimization planning basic database comprises a planning general picture, basic parameters of a power plant, system load, production decision constraint, economic and technical indexes and hydrological information; establishing a power supply optimization planning mathematical model which comprises decision variables, an objective function and constraint conditions; solving a power supply optimization planning problem by adopting a heuristic algorithm, wherein the decision content of the power supply optimization planning comprises the commissioning time and the commissioning capacity of each power plant to be selected; according to two indexes: the unit electricity cost micro-increment rate and the unit electricity cost micro-increment rate determine the production sequence of the power plant, and the electricity and electricity balance is used as the termination condition of the production. According to the power supply optimization planning method, the power supply optimization planning scheme including the renewable energy power plant is obtained through modeling of investment and operation cost calculation of the power plant and quantification of renewable energy excitation measures.
Description
【Technical field】
The invention belongs to electric power system power source optimization planning field, more particularly to a kind of power supply for considering regenerative resource are excellent
Change planing method.
【Background technology】
Electric power system power source optimization planning is the important component of power system development planning, and its main task is basis
Workload demand prediction in the system several years, under conditions of future load demand is met, it is considered to which various constraintss are (e.g., electric
Source engineering schedule, investment and recovery, system reliability requirement), seek an optimal power sources construction programme, determine when where
Build which kind of type, the power plant of many Large Copacities.At present, optimal power sources construction programme is mainly social benefit preferably, and meets
The scheme of certain reliability index requirements and construction of environmental protection requirement, to meet demand of all trades and professions to rational utilization of electricity.
With the continuous progress of human civilization, industrialization and the continuous improvement of automatization level, social development is for energy
The demand in source is increasing, so as to bring fossil energy scarcity, the serious problems of increasing environmental pollution.Past with coal,
It is non-renewable energy resources based on the conventional energy resources such as oil, natural gas, so finding and being become at present using regenerative resource
The problem of various countries' urgent need to resolve.
At present, the renewable energy power generation such as wind energy, solar energy occupies critical role in modern power systems.Although can
Renewable source of energy generation industry development is swift and violent, but due to there are problems that lacking planning, unordered exploitation, relevant policies, can
Renewable source of energy generation plan with grid-connected difficult problem it is short-term within be difficult solve.Therefore the effect of renewable energy power generation is correctly assessed
Benefit, considers the reasonable disposition of renewable energy source current and other normal power supplies as a whole, strengthens renewable energy source current and power network
Unified planning, is the key for promoting the whole regenerative resource industrial harmonization development of China.
With the continuous progress of renewable energy power generation technology, single regenerative resource power plant scale and wind power base scale
Increasing, renewable energy power generation proportion increases increasingly in power system, and renewable energy power generation is to power system
Influence is become increasingly conspicuous, and new requirement is proposed to traditional power source planning.Therefore, needing to take into full account in power source planning can be again
The influence that the raw energy generates electricity, power supply architecture of making rational planning for studies the theoretical and square of the power source planning containing renewable energy power generation
Method.
【The content of the invention】
It is an object of the invention to provide a kind of electricity optimization planing method for considering regenerative resource.This method is with economy
Property it is optimal be target, investing to build the time and invest to build capacity and optimize to each power plant to be selected.
To achieve the above object, the present invention is adopted the following technical scheme that：
A kind of electricity optimization planing method for considering regenerative resource, comprises the following steps：
Step 1：Gather information generation electricity optimization foundation of planning database, including following 6 category information：
(1) general picture is planned：Existing power plant number NGO, power plant's number NGN to be selected, system power plant sum NGS and system rule
Draw year NT；
(2) power plant's basic parameter：
Power plant i's：Power plant type kindi, service life NIi, power plant single-machine capacity Capi, in the maximum allowable of t
Operation number of units Mti, maximum installation number of units Ngi, first batch of unit earliest allow to invest to build time CYi、CO2Emission testing cycle
SO2Emission testing cycleDischarged nitrous oxides equivalentCO Emission testing cyclesAnd lime-ash and cigarette
Dirt Emission testing cycle
(3) system loading：
Project period t：System maximum predicted load Dt, system prediction electricity Et, capacity reserve factor RDtAnd electricity
Measure reserve factor REt；
(4) Production decision-making is constrained：
Emission j regulation maximum emission EMAXj, defined t regenerative resources installation ratio ρtAnd it is defined
Regenerative resource total installed capacity ratio ρ, wherein, emission j is CO2、SO2, nitrogen oxides, CO and lime-ash and flue dust；
(5) economic and technical norms：
Discount rate I；
Power plant i investment running cost：In τ investment flow during t operation unitsAverage to one unit
Subsidy for capital expenditure SUBIi, every unit fixed operating cost FOi, every unit variable operation take VOi, power plant i is averagely to one
The insurance premium INS of uniti, the average fuel reserve expense FST to a uniti, every unit operation maintenance expense fixed part
Divide OPEFi, variable part OPEVi, average to one unit wage pay WAGi, the average administrative spending to a unit
ADCi, every unit fuel cost FUCiAnd the finance and tax of average to one unit pay TAXi；
Power plant i investment and operation subsidy：Subsidy for capital expenditure SUBIi, average to one unit capacity subsidize SUBPi, capacity
Subsidize coefficientThe generated energy of average to one unit subsidizes SUBEiAnd generated energy subsidy coefficient
Region L's：CO2Discharge punishment coefficientSO2Discharge punishment coefficientNitrogen oxides is arranged
Put punishment coefficientCO discharge punishment coefficientsAnd lime-ash and soot emissions punishment coefficient
(6) hydrographic information：
Hydroelectric power plant i envisions the HPK that exerts oneself in the low flow year of the m monthsim, normal flow year anticipation exerts oneself HPPim, the low flow year give electricity
HEKim, normal flow year give electricity HEPim；
Step 2：Set up electricity optimization mathematics for programming model, including following three part：
(1) decision variable：
The decision variable of electricity optimization planning is the integer for representing power plant i to be selected in t newly-increased operation unit number of units
Variable Xti；
(2) object function：
The object function of electricity optimization plan model is：
In formula, atiRepresent that investment cost conversions of the power plant i in t one unit of operation waits year value in lifetime,
Its calculation formula is as follows：
Wherein：Represent power plant i investment flows in τ in t operation units；To conventional fuel power plant,
Pollution treatment device investment and additional Transmission Investment including power plant i；To regenerative resource power plant,Including its subsidy for capital expenditure
SUBIi；SUBIiRepresent to subsidize the disposable fund that power plant invests, unit is ten thousand yuan, if SUBIiOccur in the form of negative value,
Then represent to subsidize investment, invested equivalent to power plant is reduced；
CRF represents recovery of the capital coefficient, and its calculation formula is as follows：
ctiThe summation of annual operating cost present worths of the power plant i in t one unit of operation within project period is represented, it is calculated
Formula is as follows：
Wherein：FOiRepresent the fixed operating cost of mono- unit of power plant i, VOiRepresent the variable operation of mono- unit of power plant i
Take；
(3) constraints：
The constraints of electricity optimization planning includes：Unit installation construction requirements to be selected, system operation constraint, environmental constraints
And regenerative resource real permeability；
Step 3：Electricity optimization planning problem is solved using heuritic approach：
The content of policy decision of electricity optimization planning includes investing to build for each power plant to be selected and the time and invests to build capacity；With two fingers
Mark：Unit quantity of electricity expense tiny increment and unit of power expense tiny increment determine the production sequence of power plant, with balance of electric power and ener
It is used as the end condition of operation；
Unit quantity of electricity expense tiny increment refers to averagely per generating a kilowatt more, and the increment that the year such as system total cost is worth, abbreviation degree is electric
Cost；Unit of power expense tiny increment refers to the installation averagely per many one kilowatts, the year such as system total cost value increment, referred to as kilowatt into
This；
Degree electric cost φs of the power plant i in t one unit of operationtiCalculation formula be：
Kilowatt cost ψs of the power plant i in t one unit of operationtiCalculation formula be：
The step of heuritic approach solves electricity optimization planning problem is as follows：
3.1 step：Read in electricity optimization foundation of planning database information；
3.2 step：Each power plant is calculated in the electric cost of degree gone into operation every year and kilowatt cost, to all power plant according to the electric cost of degree
Sorted respectively with kilowatt cost；
3.3 step：According to regenerative resource permeability index, the annual regenerative resource power plant at least to be gone into operation is calculated, it is excellent
First go into operation；
3.4 step：The electrical demand and electricity needs of computing system；
3.5 step：Gone into operation successively unit according to the electric cost sequence of degree, be unsatisfactory for constraints and jump to next power plant, directly
Met to electrical demand；
3.6 step：Gone into operation successively unit according to the sequence of kilowatt cost, be unsatisfactory for constraints and jump to next power plant, directly
Met to electricity needs.
The present invention further improvement is that, in step 2, the fixed operating cost FO of mono- unit of power plant iiIncluding average to one
The insurance premium INS of platform uniti, fuel reserve expense FSTi, operation maintenance expense fixed part OPEFi, wage WAGi, administration opens
Branch ADCi, and the fixed subsidy SUBP related to power plant capacityi, its calculation formula is as follows：
FOi=INSi+FSTi+OPEFi+WAGi+ADCi+SUBPi (5)
In formula：SUBPiThe part directly related with power plant capacity, its calculation formula in incentive measure for quantifying power plant
It is as follows：
In formula：Expression capacity subsidy coefficient, member/kilowatt；
The variable operation of mono- unit of power plant i takes VOiInclude the fuel cost FUC of every uniti, operation maintenance expense it is variable
Part OPEVi, blowdown punishment PUNiAnd the variable subsidy SUBE related to generated energyi, its calculation formula is as follows：
VOi=FUCi+OPEVi+PUNi+SUBEi (7)
In formula：PUN is punished in blowdowniIt is the punishment of the pollutant emission to each power plant, wherein, pollutant includes CO2、
SO2, CO, nitrogen oxides, lime-ash and flue dust；PUNiCalculation formula it is as follows：
Wherein：May be different in view of all kinds of emissions punishment coefficient of different zones, to the emission X's of different zones
Punish coefficientDistinguish, member/kg；{T}LIt is the set of all fuel plants in the L of region, i ∈ { T }LRepresent to every
Individual power plant i, blowdown punishment is calculated with its region L corresponding position penalty factor；Represent power plant i emission X's
Discharge capacity, emission X refers to CO2、SO2, nitrogen oxides, CO and lime-ash and flue dust；
FUELtiIt is Fuel Consumptions of the power plant i in t, is the result of calculation of Stochastic Production Simulation feedback；
SUBEiPower generating capacity subsidy is represented, it is used to quantifying in the incentive measure of power plant and the direct phase of power generating capacity
The part of pass, calculation formula is as follows：
In formula：HtiRepresent annual utilization hours.
The present invention further improvement is that, in step 2, unit installation construction requirements to be selected are specific as follows：
From the definition of investment decision variable, XtiInteger is necessary for, and value is not less than 0；Power plant goes into operation machine every year
The number of units of group should be constructed and manufacturing capacity allows operation number of units MtiLimitation, i.e.,
Xti≤Mti (10)
Given maximum installation number of units N is not to be exceeded in the total installed capacity number of units of power plantgi, i.e.,
Power plant's First unit invests to build that it is late and invests to build the time earliest in the permission of the power plant
NYSTi|≥CYi (12)
System operation constraint includes power balance and electric quantity balancing, specific as follows：
Power balance refers to：All power plant i meet workload demand in the t summations of exerting oneself provided：
In formulaRepresent the summation of exerting oneself that all power plant i are provided in t；NGtiRefer to power plant i in t
The total number of units of military service unit, WtiRefer to t power plant i a unit provide exert oneself, for thermal power plant, Natural Gas Power Plant, core
Power plant, Pumped Storage Plant, WtiIts nominal output is taken, for hydroelectric power plant, WtiIts low flow year is taken to envision the HPK that exerts oneselfim, for can
Renewable sources of energy power plant, WtiTake its confidence capacity；
DtRepresent that t predicts peak load, DctThe net power transmission loads of t are represented, regulation electricity sent outside is just；
NGtiMilitary service unit total number of units of the power plant i in t is represented, for existing power plant, NGtiCount and power plant i is moved back
Labour and enlarging situation；For power plant to be selected, NGtiIt is t and power plant i is newly equipped with board number X every year in the pasttiSummation：
Electric quantity balancing refers to：All power plant i meet electrical demand in the t electricity summations provided：
In formula：Represent the electricity summation that all power plant i are provided in t, EtiRefer to t power plant i's
The electricity that one unit is provided, Eti=Wti·Hti；HtiRefer to power plant i in t annual utilization hours, asked by production simulation
；
EtRepresent that t predicts electricity, EctThe net power transmission electricity of t is represented, regulation electricity sent outside is just；
Environmental constraints include the constraint of all kinds of emissions and the constraint of area alignment total amount, specific as follows：
All kinds of emission constraints are specific as follows：
EMijt≤EMAXj,i∈{T}L,j∈{Emis},t∈[1,NT] (16)
The constraint of area alignment total amount is specific as follows：
The total emission volumn of all kinds of emissions is no more than the defined discharge upper limit in each region：
Regenerative resource real permeability is specific as follows：
Regenerative resource year installed capacity accounting, regenerative resource accounts for t systems in t installed capacity and increased newly
The ratio of installed capacity is not less than real permeability ρ as defined in tt：
Total installation of generating capacity in regenerative resource total installed capacity accounting, regenerative resource project period accounts for the ratio of system scale not
Less than defined real permeability ρ：
Compared with prior art, the beneficial effects of the present invention are：
Modeling of the invention by being invested to power plant and operating cost is calculated, and to the amount of regenerative resource incentive measure
Change, the time of investing to build of balance regenerative resource power plant obtains including renewable energy with capacity, the corresponding power supply architecture of configuration is invested to build
The electricity optimization programme of source power plant.
The present invention can optimize planning to the system comprising regenerative resource, meanwhile, investment and operation to power plant
Expense is calculated and modeled, and regenerative resource incentive measure is quantified, so as in power plant's investment and operating cost
Calculating fall into a trap and incentive measure influence, and analysis can be made to the effect of environmental policy and economic situation.
【Brief description of the drawings】
Fig. 1 is electricity optimization planning algorithm general flow chart of the present invention；
Fig. 2 is equalization process flow chart of the present invention；
Fig. 3 is power balance process flow diagram flow chart of the present invention；
Fig. 4 is electricity optimization planning algorithm detail flowchart of the present invention.
【Embodiment】
The invention will be further described below in conjunction with the accompanying drawings.
Referring to Fig. 1 to Fig. 4, a kind of electricity optimization planing method for considering regenerative resource of the present invention comprises the following steps：
1st, collection information formation electricity optimization foundation of planning database：
Electricity optimization foundation of planning database includes following 6 class data：
(1) general picture is planned：Existing power plant number NGO, power plant's number NGN to be selected, system power plant sum NGS, systems organization
Year NT.
(2) power plant's basic parameter：
Power plant i's：Power plant type kindi, service life NIi, power plant single-machine capacity Capi, in the maximum allowable of t
Operation number of units Mti, maximum installation number of units Ngi, first batch of unit earliest allow to invest to build time CYi、CO2Emission testing cycle
SO2Emission testing cycleDischarged nitrous oxides equivalentCO Emission testing cyclesAnd lime-ash and cigarette
The Emission testing cycles such as dirt
(3) system loading：
Project period t：System peak load Dt, system prediction electricity Et, capacity reserve factor RDt, the standby system of electricity
Number REt。
(4) Production decision-making is constrained：
Emission j regulation maximum emission EMAXj, defined t regenerative resources installation ratio ρt, it is defined can
Renewable sources of energy total installed capacity ratio ρ, wherein, emission j is CO2、SO2, nitrogen oxides, CO and lime-ash and flue dust.
(5) economic and technical norms：
Discount rate I.
Power plant i investment running cost：In τ investment flow during t operation unitsAverage to one unit
Subsidy for capital expenditure SUBIi, every unit fixed operating cost FOi, every unit variable operation take VOi, power plant i is averagely to one
The insurance premium INS of uniti, the average fuel reserve expense FST to a uniti, every unit operation maintenance expense fixed part
Divide OPEFi, variable part OPEVi, average to one unit wage pay WAGi, the average administrative spending to a unit
ADCi, every unit fuel cost FUCi, average to one unit finance and tax expenditure TAXi。
Power plant i investment and operation subsidy：Subsidy for capital expenditure SUBIi, average to one unit capacity subsidize SUBPi, capacity
Subsidize coefficientThe generated energy of average to one unit subsidizes SUBEi, generated energy subsidy coefficient
Region L's：CO2Discharge punishment coefficientSO2Discharge punishment coefficientNitrogen oxides is arranged
Put punishment coefficientCO discharge punishment coefficientsOther emissions (lime-ash, flue dust etc.) discharge punishment system
Number
(6) hydrographic information：
Hydroelectric power plant i envisions the HPK that exerts oneself in the low flow year of the m monthsim, normal flow year anticipation exerts oneself HPPim, the low flow year give electricity
HEKim, normal flow year give electricity HEPim。
It is worth noting that, information in data above storehouse and need not all know, must be under maximum default condition
The information known is：Existing power plant number NGO, power plant's number NGN to be selected, systems organization year NT, power plant i single-machine capacity
Capi, power plant type kindi, t project period system peak load Dt, system prediction electricity Et, discount rate I.Default value is got over
Many, degree of optimization is poorer.
2nd, electricity optimization mathematics for programming model is set up：
2.1 decision variable
The decision variable of electricity optimization planning is the integer for representing power plant i to be selected in t newly-increased operation unit number of units
Variable Xti。
2.2 object function
Power source planning relates generally to multiple power supply engineering projects, and the service life and the operation time limit of these engineering projects may
It is different, therefore there are different remaining service lives in the planning end of term.The object function of electricity optimization plan model is rule
Total cost in the phase of drawing is minimum, uniformly to compare yardstick, the year value method such as our uses.It is every year within project period Deng year value expression
The expense of power system average outgo, includes the fixation annual cost and the annual operating cost evened up of engineering project.It is minimum Deng being worth in year
Mean that the expense that average out to power system is paid within project period is minimum.Therefore, the target letter of electricity optimization plan model
Number is
Wherein, atiRepresent that investment cost conversions of the power plant i in t one unit of operation waits year value in lifetime,
Represent power plant i investment flows in τ in t operation units.To conventional fuel power plant,Including
Power plant i pollution treatment device investment and additional Transmission Investment；To regenerative resource power plant,Including its subsidy for capital expenditure
SUBIi。SUBIiRepresent to subsidize the disposable fund that power plant invests, unit is ten thousand yuan.If SUBIiOccur in the form of negative value,
Then represent to subsidize investment, invested equivalent to power plant is reduced.
The Section 2 of object function is relevant with annual operating cost, due to the annual annual operating cost in each power plant phase not to the utmost
Together, thus annual operating cost should be converted to present worth year by year and the year value such as seek again.Here calculating is intended merely to annual operating cost to draw
It is flat, still seek recovery of the capital coefficient CRF with the year NT of project period：
ctiRepresent the summation of annual operating cost present worths of the power plant i in t one unit of operation within project period.
The Section 3 of object function represents the year value such as the operating cost of existing power plant within project period.
The fixed operating cost FO of mono- unit of power plant iiIncluding the average insurance premium INS to a uniti, fuel reserve takes
Use FSTi, operation maintenance expense fixed part OPEFi, wage WAGi, administrative spending ADCi, and the fixation related to power plant capacity
Subsidize SUBPi。
FOi=INSi+FSTi+OPEFi+WAGi+ADCi+SUBPi (5)
SUBPiThe part directly related with power plant capacity in incentive measure for quantifying power plant.Capacity is subsidized into coefficient(member/kilowatt) it is multiplied by the single-machine capacity Cap of power plantiIt is exactly power plant's capacity subsidy.
The variable operation of mono- unit of power plant i takes VOiInclude the fuel cost FUC of every uniti, operation maintenance expense it is variable
Part OPEVi, blowdown punishment PUNiAnd the variable subsidy SUBE related to generated energyi。
VOi=FUCi+OPEVi+PUNi+SUBEi (7)
PUN is punished in blowdowniIt is the punishment of the pollutant emission to each power plant, including CO2、SO2, CO, nitrogen oxides, ash
Slag and flue dust.PUNiCalculation formula it is as follows,
May be different in view of all kinds of emissions punishment coefficient of different zones, the punishment to the emission X of different zones
CoefficientDistinguish, unit is member/kg；{T}LIt is the set of all fuel plants in the L of region, i ∈ { T }LExpression pair
Each power plant i, blowdown punishment is calculated with its region L corresponding position penalty factor；Represent power plant i emissions X's
Emission testing cycle (kg/kg, to Natural Gas Power Plant, kg/ cubic metres), i.e., often consumption 1kg fuel (1 cubic metre of natural gas) is arranged accordingly
Thing X discharge capacity is put, emission X refers to CO2、SO2, nitrogen oxides, CO and lime-ash and flue dust.
FUELtiIt is Fuel Consumptions of the power plant i in t, is the result of calculation of Stochastic Production Simulation feedback.Stochastic production
Simulation is according to system loading curve, install scale, repair sheet, rated power, the forced outage rate of power plant, the base lotus coal of thermal power plant
Consumption rate, peak load coa consumption rate, coal price, minimum technology are exerted oneself, and hydroelectric power plant's normal flow year gives electricity HEPim, normal flow year anticipation exert oneself
HPPim, the given electricity of Pumped Storage Plant, cycle efficieny, draw water power, and regenerative resource power plant exerts oneself expectation curve, to being
The system condition of production is simulated, and obtains each power plant annual utilization hours H annual within project periodtiConsumed with the fuel of fuel plants
Measure FUELti。
SUBEiThe part directly related with power generating capacity in incentive measure for quantifying power plant.Generated energy is subsidized
Coefficient(member/kilowatt hour) is multiplied by power plant capacity C apiWith annual utilization hours HtiIt is exactly power generating capacity subsidy.
2.3 constraints
2.3.1 unit installation construction requirements to be selected
From the definition of investment decision variable, XtiInteger is necessary for, and value is not less than 0.
The number of units of power plant's operation unit every year should be constructed and manufacturing capacity allows operation number of units MtiLimitation, i.e.,
Xti≤Mti (10)
Given maximum installation number of units N is not to be exceeded in the total installed capacity number of units of power plantgi, i.e.,
Power plant's First unit invests to build that it is late and invests to build the time earliest in the permission of the power plant
NYSTi|≥CYi (12)
2.3.2 system operation is constrained
Power balance
Power balance refers to：All power plant i meet workload demand in the t summations of exerting oneself provided：
In formula：Represent the summation of exerting oneself that all power plant i are provided in t；NGtiRefer to power plant i in t
The total number of units of military service unit, WtiRefer to t power plant i a unit provide exert oneself, for thermal power plant, Natural Gas Power Plant, core
Power plant, Pumped Storage Plant, WtiIts nominal output is taken, for hydroelectric power plant, WtiIts low flow year is taken to envision the HPK that exerts oneselfim, for can
Renewable sources of energy power plant, WtiTake its confidence capacity.
DtRepresent that t predicts peak load, DctThe net power transmission loads of t are represented, regulation electricity sent outside is just.
NGtiRepresent military service unit total number of units of the power plant i in t.For existing power plant, NGtiCount and power plant i is moved back
Labour and enlarging situation；For power plant to be selected, NGtiIt is t and power plant i is newly equipped with board number X every year in the pasttiSummation：
Electric quantity balancing
Electric quantity balancing refers to：All power plant i meet electrical demand in the t electricity summations provided：
In formula：Represent the electricity summation that all power plant i are provided in t, EtiRefer to t power plant i's
The electricity that one unit is provided, Eti=Wti·Hti；HtiRefer to power plant i in t annual utilization hours, asked by production simulation
.
EtRepresent that t predicts electricity, EctThe net power transmission electricity of t is represented, regulation electricity sent outside is just.
2.3.3 environmental constraints
1) all kinds of emission constraints
EMijt≤EMAXj,i∈{T}L,j∈{Emis},t∈[1,NT] (16)
2) area alignment total amount is constrained
The total emission volumn of all kinds of emissions is no more than the defined discharge upper limit in each region：
2.3.4 regenerative resource real permeability
Regenerative resource year installed capacity accounting
Regenerative resource is advised in the ratio that t installed capacity accounts for t system adding new capacities not less than t
Fixed real permeability ρt：
Regenerative resource total installed capacity accounting
The ratio that total installation of generating capacity in regenerative resource project period accounts for system scale is not less than defined real permeability
ρ：
3rd, heuritic approach solves electricity optimization planning problem：
Electricity optimization planning problem is under certain constraints, to meet systematic electricity electrical demand, to being needed
The Optimal Decision-making scheme that power plant makes is selected, the content of policy decision of electricity optimization planning invests to build time and throwing including each power plant to be selected
Build capacity.With two indices：Unit quantity of electricity expense tiny increment, unit of power expense tiny increment determine the production sequence of power plant,
The end condition of operation is used as using balance of electric power and ener.
Unit quantity of electricity expense tiny increment refers to averagely often to generate a kilowatt more, the increment that the year such as system total cost is worth, hereinafter
The electric cost of degree.Unit of power expense tiny increment refers to the installation averagely per many one kilowatts, and the increment that system total cost etc. is worth in year is simple hereinafter
Claim kilowatt cost.
Degree electric cost φs of the power plant i in t one unit of operationtiCalculation formula be：
Kilowatt cost ψs of the power plant i in t one unit of operationtiCalculation formula be：
The step of heuritic approach solves electricity optimization planning problem is as follows：
(1) electricity optimization foundation of planning database information is read in；
(2) calculate each power plant in the electric cost of degree gone into operation every year and kilowatt cost, to all power plant according to the electric cost of degree with
Kilowatt cost sorts respectively；
(3) according to regenerative resource permeability index, the annual regenerative resource power plant at least to be gone into operation is calculated, it is preferential to throw
Production；
Note：It not is to be uniquely determined by regenerative resource permeability index that whether regenerative resource power plant, which goes into operation, here
Simply for the permeability index of guarantee regenerative resource, a part of regenerative resource power plant of preferentially going into operation.All power plant are joined jointly
With the cost calculation and sequence of (2) step, there is equal operation chance in (5) (6) step.
(4) electrical demand and electricity needs of computing system；
(5) gone into operation successively unit according to the electric cost sequence of degree, be unsatisfactory for constraints and jump to next power plant, Zhi Dao electricity
Measure need satisfaction；
(6) gone into operation successively unit according to the sequence of kilowatt cost, be unsatisfactory for constraints and jump to next power plant, Zhi Dao electricity
Power need satisfaction.
Claims (1)
1. a kind of electricity optimization planing method for considering regenerative resource, it is characterised in that comprise the following steps：
Step 1：Gather information generation electricity optimization foundation of planning database, including following 6 category information：
(1) general picture is planned：Existing power plant number NGO, power plant's number NGN to be selected, system power plant sum NGS and systems organization year
Number NT；
(2) power plant's basic parameter：
Power plant i's：Power plant type kindi, service life NIi, power plant single-machine capacity Capi, in t maximum allowable operation
Number of units Mti, maximum installation number of units Ngi, first batch of unit earliest allow to invest to build time CYi、CO2Emission testing cycleSO2
Emission testing cycleDischarged nitrous oxides equivalentCO Emission testing cyclesAnd lime-ash and flue dust
Emission testing cycle
(3) system loading：
Project period t：System maximum predicted load Dt, system prediction electricity Et, capacity reserve factor RDtAnd electricity is standby
Coefficients REt；
(4) Production decision-making is constrained：
Emission j regulation maximum emission EMAXj, defined t regenerative resources installation ratio ρtAnd it is defined can be again
Raw energy total installed capacity ratio ρ, wherein, emission j is CO2、SO2, nitrogen oxides, CO and lime-ash and flue dust；
(5) economic and technical norms：
Discount rate I；
Power plant i investment running cost：In τ investment flow during t operation unitsThe average investment to a unit
Subsidize SUBIi, every unit fixed operating cost FOi, every unit variable operation take VOi, power plant i is averagely to a unit
Insurance premium INSi, the average fuel reserve expense FST to a uniti, every unit operation maintenance expense fixed part
OPEFi, variable part OPEVi, average to one unit wage pay WAGi, the average administrative spending ADC to a uniti、
The fuel cost FUC of every unitiAnd the finance and tax of average to one unit pay TAXi；
Power plant i investment and operation subsidy：Subsidy for capital expenditure SUBIi, average to one unit capacity subsidize SUBPi, capacity subsidy
CoefficientThe generated energy of average to one unit subsidizes SUBEiAnd generated energy subsidy coefficient
Region L's：CO2Discharge punishment coefficientSO2Discharge punishment coefficientDischarged nitrous oxides are punished
CoefficientCO discharge punishment coefficientsAnd lime-ash and soot emissions punishment coefficient
(6) hydrographic information：
Hydroelectric power plant i envisions the HPK that exerts oneself in the low flow year of the m monthsim, normal flow year anticipation exerts oneself HPPim, the low flow year give electricity
HEKim, normal flow year give electricity HEPim；
Step 2：Set up electricity optimization mathematics for programming model, including following three part：
(1) decision variable：
The decision variable of electricity optimization planning is the integer variable for representing power plant i to be selected in t newly-increased operation unit number of units
Xti；
(2) object function：
The object function of electricity optimization plan model is：
<mrow>
<mi>min</mi>
<mi> </mi>
<mi>B</mi>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>t</mi>
<mo>=</mo>
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<mrow>
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</munderover>
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</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>N</mi>
</mrow>
</munderover>
<msub>
<mi>a</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>C</mi>
<mi>R</mi>
<mi>F</mi>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>N</mi>
</mrow>
</munderover>
<msub>
<mi>c</mi>
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<mi>t</mi>
<mi>i</mi>
</mrow>
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<mi>i</mi>
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<mi>C</mi>
<mi>R</mi>
<mi>F</mi>
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<mi>t</mi>
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<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>T</mi>
</mrow>
</munderover>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>N</mi>
</mrow>
</munderover>
<msub>
<mi>c</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula, atiRepresent that investment cost conversions of the power plant i in t one unit of operation waits year value in lifetime, it is counted
Calculate formula as follows：
<mrow>
<msub>
<mi>a</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
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<mi>I</mi>
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<mi>i</mi>
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<mn>1</mn>
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<mi>&tau;</mi>
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<mn>1</mn>
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<mi>t</mi>
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<mi>&pi;</mi>
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<mi>t</mi>
<mi>i</mi>
</mrow>
<mrow>
<mo>(</mo>
<mi>&tau;</mi>
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</mrow>
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<msup>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<mi>I</mi>
<mo>)</mo>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>&tau;</mi>
</mrow>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：Represent power plant i investment flows in τ in t operation units；To conventional fuel power plant,Including
Power plant i pollution treatment device investment and additional Transmission Investment；To regenerative resource power plant,Including its subsidy for capital expenditure
SUBIi；SUBIiRepresent to subsidize the disposable fund that power plant invests, unit is ten thousand yuan, if SUBIiOccur in the form of negative value,
Then represent to subsidize investment, invested equivalent to power plant is reduced；
CRF represents recovery of the capital coefficient, and its calculation formula is as follows：
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<mi>C</mi>
<mi>R</mi>
<mi>F</mi>
<mo>=</mo>
<mfrac>
<mrow>
<mi>I</mi>
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<mi>I</mi>
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<mi>N</mi>
<mi>T</mi>
</mrow>
</msup>
</mrow>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<mn>1</mn>
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<mi>I</mi>
<mo>)</mo>
</mrow>
<mrow>
<mi>N</mi>
<mi>T</mi>
</mrow>
</msup>
<mo>-</mo>
<mn>1</mn>
</mrow>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
ctiRepresent the summation of annual operating cost present worths of the power plant i in t one unit of operation within project period, its calculation formula
It is as follows：
<mrow>
<msub>
<mi>c</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
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<mi>&tau;</mi>
<mo>=</mo>
<mi>t</mi>
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<mrow>
<mi>N</mi>
<mi>T</mi>
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<mo>&lsqb;</mo>
<msub>
<mi>FO</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msub>
<mi>VO</mi>
<mi>i</mi>
</msub>
<mo>&rsqb;</mo>
<msup>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<mi>I</mi>
<mo>)</mo>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>&tau;</mi>
</mrow>
</msup>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：FOiRepresent the fixed operating cost of mono- unit of power plant i, VOiRepresent that the variable operation of mono- unit of power plant i takes；
(3) constraints：
The constraints of electricity optimization planning includes：Unit installation construction requirements to be selected, system operation constraint, environmental constraints and
Regenerative resource real permeability；
Step 3：Electricity optimization planning problem is solved using heuritic approach：
The content of policy decision of electricity optimization planning includes investing to build for each power plant to be selected and the time and invests to build capacity；With two indices：It is single
Position electricity expense tiny increment and unit of power expense tiny increment determine the production sequence of power plant, and throwing is used as using balance of electric power and ener
The end condition of production；
Unit quantity of electricity expense tiny increment refers to averagely often to generate a kilowatt more, the increment that the year such as system total cost is worth, abbreviation degree electricity cost；
Unit of power expense tiny increment refers to the installation averagely per many one kilowatts, the referred to as increment that system total cost etc. is worth in year, kilowatt cost；
Degree electric cost φs of the power plant i in t one unit of operationtiCalculation formula be：
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<msub>
<mi>&phi;</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>B</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</mfrac>
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<mfrac>
<mrow>
<msub>
<mi>a</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>C</mi>
<mi>R</mi>
<mi>F</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>c</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>21</mn>
<mo>)</mo>
</mrow>
</mrow>
Kilowatt cost ψs of the power plant i in t one unit of operationtiCalculation formula be：
<mrow>
<msub>
<mi>&psi;</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mo>&part;</mo>
<mi>B</mi>
</mrow>
<mrow>
<mo>&part;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
</mfrac>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>a</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>C</mi>
<mi>R</mi>
<mi>F</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>c</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
</mfrac>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>22</mn>
<mo>)</mo>
</mrow>
</mrow>
The step of heuritic approach solves electricity optimization planning problem is as follows：
3.1 step：Read in electricity optimization foundation of planning database information；
3.2 step：Each power plant is calculated in the electric cost of degree gone into operation every year and kilowatt cost, to all power plant according to the electric cost and thousand of degree
Watt cost sorts respectively；
3.3 step：According to regenerative resource permeability index, the annual regenerative resource power plant at least to be gone into operation is calculated, it is preferential to throw
Production；
3.4 step：The electrical demand and electricity needs of computing system；
3.5 step：Gone into operation successively unit according to the electric cost sequence of degree, be unsatisfactory for constraints and jump to next power plant, Zhi Dao electricity
Measure need satisfaction；
3.6 step：Gone into operation successively unit according to the sequence of kilowatt cost, be unsatisfactory for constraints and jump to next power plant, Zhi Dao electricity
Power need satisfaction；
Wherein, in step 2, the fixed operating cost FO of mono- unit of power plant iiIncluding the average insurance premium INS to a uniti, combustion
Expect carrying costs FSTi, operation maintenance expense fixed part OPEFi, wage WAGi, administrative spending ADCi, and with power plant's capacity phase
The fixed subsidy SUBP of passi, its calculation formula is as follows：
FOi=INSi+FSTi+OPEFi+WAGi+ADCi+SUBPi (5)
In formula：SUBPiThe part directly related with power plant capacity in incentive measure for quantifying power plant, its calculation formula is as follows：
<mrow>
<msub>
<mi>SUBP</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mover>
<mrow>
<msub>
<mi>SUBP</mi>
<mi>i</mi>
</msub>
</mrow>
<mo>~</mo>
</mover>
<mo>&CenterDot;</mo>
<msub>
<mi>Cap</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：Expression capacity subsidy coefficient, member/kilowatt；
The variable operation of mono- unit of power plant i takes VOiInclude the fuel cost FUC of every uniti, operation maintenance expense variable part
OPEVi, blowdown punishment PUNiAnd the variable subsidy SUBE related to generated energyi, its calculation formula is as follows：
VOi=FUCi+OPEVi+PUNi+SUBEi (7)
In formula：PUN is punished in blowdowniIt is the punishment of the pollutant emission to each power plant, wherein, pollutant includes CO2、SO2、CO、
Nitrogen oxides, lime-ash and flue dust；PUNiCalculation formula it is as follows：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>PUN</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mo>(</mo>
<mover>
<mrow>
<msub>
<mi>PUN</mi>
<mrow>
<mi>L</mi>
<mo>,</mo>
<msub>
<mi>CO</mi>
<mn>2</mn>
</msub>
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<mo>&CenterDot;</mo>
<mover>
<mrow>
<msub>
<mi>EQU</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<msub>
<mi>CO</mi>
<mn>2</mn>
</msub>
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<mover>
<mrow>
<msub>
<mi>PUN</mi>
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<msub>
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<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein：May be different in view of all kinds of emissions punishment coefficient of different zones, the punishment to the emission X of different zones
CoefficientDistinguish, member/kg；{T}LIt is the set of all fuel plants in the L of region, i ∈ { T }LRepresent to each electricity
Factory i, blowdown punishment is calculated with its region L corresponding position penalty factor；Represent power plant i emission X discharge
Amount, emission X refers to CO2、SO2, nitrogen oxides, CO and lime-ash and flue dust；
FUELtiIt is Fuel Consumptions of the power plant i in t, is the result of calculation of Stochastic Production Simulation feedback；
SUBEiPower generating capacity subsidy is represented, it is used to quantifying portion directly related with power generating capacity in the incentive measure of power plant
Point, calculation formula is as follows：
<mrow>
<msub>
<mi>SUBE</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mover>
<mrow>
<msub>
<mi>SUBE</mi>
<mi>i</mi>
</msub>
</mrow>
<mo>~</mo>
</mover>
<mo>&CenterDot;</mo>
<msub>
<mi>Cap</mi>
<mi>i</mi>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>H</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：HtiRepresent annual utilization hours；
In step 2, unit installation construction requirements to be selected are specific as follows：
From the definition of investment decision variable, XtiInteger is necessary for, and value is not less than 0；Power plant's operation unit every year
Number of units should be constructed and manufacturing capacity allows operation number of units MtiLimitation, i.e.,
Xti≤Mti (10)
Given maximum installation number of units N is not to be exceeded in the total installed capacity number of units of power plantgi, i.e.,
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>T</mi>
</mrow>
</munderover>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>N</mi>
<mrow>
<mi>g</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
Power plant's First unit invests to build that it is late and invests to build the time earliest in the permission of the power plant
NYSTi≥CYi (12)
System operation constraint includes power balance and electric quantity balancing, specific as follows：
Power balance refers to：All power plant i meet workload demand in the t summations of exerting oneself provided：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>S</mi>
</mrow>
</munderover>
<msub>
<mi>NG</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>D</mi>
<mi>t</mi>
</msub>
<mo>-</mo>
<msub>
<mi>D</mi>
<mrow>
<mi>c</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>D</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
In formulaRepresent the summation of exerting oneself that all power plant i are provided in t；NGtiRefer to military services of the power plant i in t
The total number of units of unit, WtiRefer to that a t power plant i unit provides exerts oneself, and for thermal power plant, Natural Gas Power Plant, nuclear power plant, takes out
Water storage power plant, WtiIts nominal output is taken, for hydroelectric power plant, WtiIts low flow year is taken to envision the HPK that exerts oneselfim, for regenerative resource
Power plant, WtiTake its confidence capacity；
DtRepresent that t predicts peak load, DctThe net power transmission loads of t are represented, regulation electricity sent outside is just；
NGtiMilitary service unit total number of units of the power plant i in t is represented, for existing power plant, NGtiCount and power plant i retired and expand
Build situation；For power plant to be selected, NGtiIt is t and power plant i is newly equipped with board number X every year in the pasttiSummation：
<mrow>
<msub>
<mi>NG</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>&tau;</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>t</mi>
</munderover>
<msub>
<mi>X</mi>
<mrow>
<mi>&tau;</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>14</mn>
<mo>)</mo>
</mrow>
</mrow>
Electric quantity balancing refers to：All power plant i meet electrical demand in the t electricity summations provided：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>S</mi>
</mrow>
</munderover>
<msub>
<mi>NG</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mi>t</mi>
</msub>
<mo>-</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>c</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>+</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>E</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>15</mn>
<mo>)</mo>
</mrow>
</mrow>
In formula：Represent the electricity summation that all power plant i are provided in t, EtiRefer to a t power plant i machine
The electricity that group is provided, Eti=Wti·Hti；HtiRefer to annual utilization hours of the power plant i in t, tried to achieve by production simulation；
EtRepresent that t predicts electricity, EctThe net power transmission electricity of t is represented, regulation electricity sent outside is just；
Environmental constraints include the constraint of all kinds of emissions and the constraint of area alignment total amount, specific as follows：
All kinds of emission constraints are specific as follows：
EMijt≤EMAXj,i∈{T}L,j∈{Emis},t∈[1,NT] (16)
<mrow>
<msub>
<mi>EM</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<mover>
<mrow>
<msub>
<mi>EQU</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mo>~</mo>
</mover>
<mo>&CenterDot;</mo>
<msub>
<mi>FUEL</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>17</mn>
<mo>)</mo>
</mrow>
</mrow>
The constraint of area alignment total amount is specific as follows：
The total emission volumn of all kinds of emissions is no more than the defined discharge upper limit in each region：
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>&Element;</mo>
<msub>
<mrow>
<mo>{</mo>
<mi>T</mi>
<mo>}</mo>
</mrow>
<mi>L</mi>
</msub>
</mrow>
</munder>
<msub>
<mi>EM</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
<mi>t</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>EMAX</mi>
<mrow>
<mi>j</mi>
<mi>L</mi>
</mrow>
</msub>
<mo>,</mo>
<mi>j</mi>
<mo>&Element;</mo>
<mo>{</mo>
<mi>E</mi>
<mi>m</mi>
<mi>i</mi>
<mi>s</mi>
<mo>}</mo>
<mo>,</mo>
<mi>t</mi>
<mo>&Element;</mo>
<mo>&lsqb;</mo>
<mn>1</mn>
<mo>,</mo>
<mi>N</mi>
<mi>T</mi>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>18</mn>
<mo>)</mo>
</mrow>
</mrow>
Regenerative resource real permeability is specific as follows：
Regenerative resource year installed capacity accounting, regenerative resource accounts for the newly-increased installation of t systems in t installed capacity
The ratio of capacity is not less than real permeability ρ as defined in tt：
<mrow>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>&Element;</mo>
<mo>{</mo>
<mi>R</mi>
<mo>}</mo>
</mrow>
</munder>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&rho;</mi>
<mi>t</mi>
</msub>
<mo>&CenterDot;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>N</mi>
</mrow>
</munderover>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>19</mn>
<mo>)</mo>
</mrow>
</mrow>
The ratio that total installation of generating capacity in regenerative resource total installed capacity accounting, regenerative resource project period accounts for system scale is not less than
Defined real permeability ρ：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>T</mi>
</mrow>
</munderover>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>&Element;</mo>
<mo>{</mo>
<mi>R</mi>
<mo>}</mo>
</mrow>
</munder>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<mi>&rho;</mi>
<mo>&CenterDot;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>T</mi>
</mrow>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>G</mi>
<mi>N</mi>
</mrow>
</munderover>
<msub>
<mi>X</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>W</mi>
<mrow>
<mi>t</mi>
<mi>i</mi>
</mrow>
</msub>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>20</mn>
<mo>)</mo>
</mrow>
<mo>.</mo>
</mrow>
5
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| CN107194539B (en) * | 2017-04-19 | 2020-08-25 | 中国能源建设集团浙江省电力设计院有限公司 | Electric power and electric quantity balancing method considering prevention and control of atmospheric pollutants |
| CN107093029A (en) * | 2017-05-03 | 2017-08-25 | 国家电网公司 | A kind of power source planning method constrained based on Renewable energy policy control |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1916957A (en) * | 2006-09-07 | 2007-02-21 | 上海交通大学 | Environmental protection united programming method for power supply based on extended sorting method |
| EP1887486A1 (en) * | 2006-08-04 | 2008-02-13 | General Electric Company | Power generation mix forecasting modeling method |
-
2014
- 2014-07-30 CN CN201410370735.3A patent/CN104123596B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1887486A1 (en) * | 2006-08-04 | 2008-02-13 | General Electric Company | Power generation mix forecasting modeling method |
| CN1916957A (en) * | 2006-09-07 | 2007-02-21 | 上海交通大学 | Environmental protection united programming method for power supply based on extended sorting method |
Non-Patent Citations (1)
| Title |
|---|
| 电源优化模型;王锡凡;《西安交通大学学报》;19860430;第20卷(第2期);第1-12页 * |
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