CN108268973A - Uncertain two benches chance constraint low-carbon electric power Method for optimized planning - Google Patents

Uncertain two benches chance constraint low-carbon electric power Method for optimized planning Download PDF

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CN108268973A
CN108268973A CN201711379937.4A CN201711379937A CN108268973A CN 108268973 A CN108268973 A CN 108268973A CN 201711379937 A CN201711379937 A CN 201711379937A CN 108268973 A CN108268973 A CN 108268973A
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周长玉
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North China Electric Power University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier

Abstract

The invention belongs to power planning technical field more particularly to a kind of uncertain two benches chance constraint low-carbon electric power Method for optimized planning, including：According to the history and prediction data that are obtained in system and load prediction system is dispatched in intelligent grid, and combine the index of energy-saving and emission-reduction and carbon emission transaction value data, using profit maximization as target, low-carbon electric power plan model is established；Electricity needs, carbon emission quota, super generating risk are included in by planning using chance constraint, and use two stage stochastic programming method, two benches chance constraint low-carbon electric power plan model is established in optimization, will be had probabilistic parameter setting in model into range format, is obtained stablizing feasible interval solutions.System benefit and environmental goals can be weighed with aid decision making personnel to the obtained section solution of different decision objectives project period, relatively reliable scheme is provided for power generation, the economic benefit for mentioning electric power enterprise is not only facilitated, and contributes to the sustainable development of regional economy.

Description

Uncertain two benches chance constraint low-carbon electric power Method for optimized planning
Technical field
The invention belongs to power planning technical field more particularly to a kind of uncertain two benches chance constraint low-carbon electric powers Method for optimized planning.
Background technology
CO2Prediction emission is a complicated system engineering, has the characteristics that dynamic, uncertainty.With carbon It merchandises and being implemented in full in Chinese, the electric power enterprise of one of carbon emission key industry will face the external factor of more complexity.This Outside, the initial allocation of quota mode of carbon emission transaction also can later change with starting period first stage, the ratio of free quota Example will be declined after carbon transaction mechanism marches toward the maturity period, however the difference of quota allocation policy, and electric power enterprise can be produced Planning will exert far reaching influence.Rationally effective Power System Planning and operation method will ensure that electric system is steady in safety The powerful measure of efficient carbon emission reduction is realized under the premise of fixed operation.Power industry is one of key industry of carbon emission, wherein, Fossil coal fired power generation is the main source for causing atmosphere pollution and greenhouse gas emission.How to optimize allocation of resources, rationally advise It draws, so that electric system profit maximization is business and government under the premise of meeting energy demand, total carbon emission is reduced The major issue of common concern.
At present, energy models have become the analysis tool of standard, and policymaker can effectively handle energy from energy models ENERGY PLANNING, optimization and forecasting problem in the system of source, these methodologies are contributed to by explaining the complexity of energy resource system Examine and identify the effect of various expectation management strategies.As LEAP models,
MARKAL models, MESSAGE models etc..The research tool of mainstream is the thinking using Optimized model at present, but is worked as Under conditions of a large amount of policy variables and technological progress, existing simple model can not reflect completely production of energy pattern, Interaction between consumer technology and policy variable.As LEAP models are difficult to fully consider technological progress and the implementation of policy Cost, it is impossible to reaction market supply and demand signal completely.MARKAL models then need a large amount of input data, due to by model calculation The limitation of ability and availability, some data are had to using average value, this can influence the calculating knot of different condition drag Fruit.
In addition to this, though researcher from different perspectives studies carbon transaction market access, rare analysis at present is not With carbon transaction policy situation to power planning and CO2It is each that region low-carbon electric power production can not be provided previously in the influence of discharge capacity The optimization condition of production under class resource allocation programming scheme and different carbon quota situations；Also it is rare by electricity needs, super generating wind The uncertain factors such as danger are included in the correlative study of power generation planning.Since the carbon emission trade system of China is still in starting Stage, transaction and allocation of quota mechanism are not perfect enough.Therefore, many conditions such as different, electricity needs variation of research carbon emission policy Under low-carbon electric power plan optimization method, carbon emission is reduced and comprehensive carry out in carbon transaction market has important reality to anticipate Justice.
Invention content
In view of the above-mentioned problems, the present invention, which proposes the present invention, proposes a kind of uncertain two benches chance constraint low-carbon electricity Power Method for optimized planning, including：
According to the history and prediction data obtained in scheduling system in intelligent grid and load prediction system, and combine section The energy index of emission reduction and carbon emission transaction value data, using profit maximization as target, establish low-carbon electric power plan model；It adopts Electricity needs, carbon emission quota, super generating risk are included in planning, and use two stage stochastic programming method with chance constraint, it is excellent Two benches chance constraint low-carbon electric power plan model is established in change, will have probabilistic parameter setting in model into section shape Formula obtains stablizing feasible interval solutions.
The profit computational methods are：The income that carbon dioxide is expected corresponding to yield surpasses expection plus carbon dioxide Income corresponding to yield subtracts cost produced by reducing emission of carbon dioxide, then subtracts the dioxy beyond free carbon emission quota Change carbon emission transaction cost, if the CO2 emission transaction cost beyond free carbon emission quota is negative, show Also extra carbon emission power is used to merchandise to be converted to profit.
The chance constraint includes：Total amount constraint, generated energy and electricity needs between generated energy and available power generation capacity Between power supply and demand balance constraint, CO2 emissions constraint, the nonnegativity restrictions of generated energy and electricity needs.
The two benches chance constraint low-carbon electric power plan model includes：
Maxf=max (f1+f2-f3-f4) (1a)
Wherein, f represents profit, f1Represent that carbon dioxide is expected the income corresponding to yield, f2Represent that carbon dioxide surpasses It is expected that the income corresponding to yield, f3Cost produced by representing reducing emission of carbon dioxide, f4It represents beyond free carbon emission quota CO2 emission transaction cost；I=1,2,3 ..., I represent period sequence, and j=1,2,3 ..., J represent ground region sequence, k =1,2,3 ..., K represent electricity power enterprise's sequence, APijkExpection carbon dioxide year yield for i period j area k electricity power enterprises It is used as first stage decision variable, CIijkUnit carbon dioxide year for i period j area k electricity power enterprises is corresponding to yield Income, E [] be expectation of a random variable；BQijkElectricity needs to work as i periods j area is DMij(kwh/y) when, k hairs Electric enterprise is more than APijkCarbon dioxide yield is used as second stage decision variable caused by power generation；GQijk=APijk+ BQijkFor the total yield of i period j area k electricity power enterprise's carbon dioxide；ηijkEmission reduction for i period j area k electricity power enterprises is imitated Rate； RCijkUnit carbon dioxide emission reduction cost for i period j city k electricity power enterprises；EAijkFor i period j area k electricity power enterprises The free carbon emission quota provided in advance；BPijkThe settlement price of right to emit carbon dioxide is bought for i period j area k electricity power enterprises Lattice；
Constraints is：
(1) total amount constrains
GCijk≤CAijk (1b)
GCijk=(GQijk/(DSijkEL)) (1b_1)
GCijkGenerated energy for i period j area k electricity power enterprises；DSijkFor unit generated energy coal consumption；EL is carbon dioxide Emission factor, unit 104ton/kwh；CAijkAvailable power generation capacity for i period j area k electricity power enterprises；
(2) power supply and demand balance constrains
DMijTotal electricity demand for the j area planning phases
(3) region CO2 emission constrains
ERkFor i period j regional power system CO2The discharge capacity upper limit
QjFor the voluntary emission reduction of authentication in the j area planning phases, unit 104ton；
(4) nonnegativity restrictions
APijk≥BQijk≥0 (1f)。
The beneficial effects of the present invention are：
Bring the uncertain factors such as electricity needs, carbon emission quota allocation policy, super generating risk into different project periods In the low-carbon electric power planning of region, with two stage stochastic programming, establish based on low-carbon electric power planing method, analyze and research excellent The resource allocation proposal of change, to provide support for power industry production programming decision.Building can optimize under condition of uncertainty Each enterprise's power generation resource distribution in region, is included in planning process by uncertainty, can improve the section of programme implementation result The property learned and confidence level.The obtained section solution of different decision objectives project period with aid decision making personnel can be weighed and be Benefit of uniting and environmental goals, relatively reliable scheme is provided for power generation, not only facilitates the economic effect for mentioning electric power enterprise Benefit, and contribute to the sustainable development of regional economy.
Description of the drawings
Fig. 1 is the optimization generated energy schematic diagram of carbon emission quota Free distribution scene Xia Ge enterprises
Fig. 2 increases CO caused by power generation newly for second stage2Measure schematic diagram
Fig. 3 halves scene carbon dioxide yield schematic diagram for free carbon emission quota
Fig. 4 is the system benefit figure under different allocation plans
Specific embodiment
Below in conjunction with the accompanying drawings, it elaborates to embodiment.
The present invention proposes a kind of uncertain two benches chance constraint low-carbon electric power Method for optimized planning, including：
According to the history and prediction data obtained in scheduling system in intelligent grid and load prediction system, and combine section The energy index of emission reduction and carbon emission transaction value data, using profit maximization as target, establish low-carbon electric power plan model；It adopts Electricity needs, carbon emission quota, super generating risk are included in planning, and use two stage stochastic programming method with chance constraint, it is excellent Two benches chance constraint low-carbon electric power plan model is established in change, will have probabilistic parameter setting in model into section shape Formula obtains stablizing feasible interval solutions.
The profit computational methods are：The income that carbon dioxide is expected corresponding to yield surpasses expection plus carbon dioxide Income corresponding to yield subtracts cost produced by reducing emission of carbon dioxide, then subtracts the dioxy beyond free carbon emission quota Change carbon emission transaction cost, if the CO2 emission transaction cost beyond free carbon emission quota is negative, show Also extra carbon emission power is used to merchandise to be converted to profit.
The chance constraint includes：Total amount constraint, generated energy and electricity needs between generated energy and available power generation capacity Between power supply and demand balance constraint, CO2 emissions constraint, the nonnegativity restrictions of generated energy and electricity needs.
The two benches chance constraint low-carbon electric power plan model includes：
Maxf=max (f1+f2-f3-f4) (1a)
Wherein, f represents profit, f1Represent that carbon dioxide is expected the income corresponding to yield, f2Represent that carbon dioxide surpasses It is expected that the income corresponding to yield, f3Cost produced by representing reducing emission of carbon dioxide, f4It represents beyond free carbon emission quota CO2 emission transaction cost；I=1,2,3 ..., I represent period sequence, and j=1,2,3 ..., J represent ground region sequence, k =1,2,3 ..., K represent electricity power enterprise's sequence, APijkExpection carbon dioxide year yield for i period j area k electricity power enterprises It is used as first stage decision variable, CIijkUnit carbon dioxide year for i period j area k electricity power enterprises is corresponding to yield Income, E [] be expectation of a random variable；BQijkElectricity needs to work as i periods j area is DMij(kwh/y) when, k hairs Electric enterprise is more than APijkCarbon dioxide yield is used as second stage decision variable caused by power generation；GQijk=APijk+ BQijkFor the total yield of i period j area k electricity power enterprise's carbon dioxide；ηijkEmission reduction for i period j area k electricity power enterprises is imitated Rate； RCijkUnit carbon dioxide emission reduction cost for i period j city k electricity power enterprises；EAijkFor i period j area k electricity power enterprises The free carbon emission quota provided in advance；BPijkThe settlement price of right to emit carbon dioxide is bought for i period j area k electricity power enterprises Lattice；
Constraints is：
(5) total amount constrains
GCijk≤CAijk (1b)
GCijk=(GQijk/(DSijkEL)) (1b_1)
GCijkGenerated energy for i period j area k electricity power enterprises；DSijkFor unit generated energy coal consumption；EL is carbon dioxide Emission factor, unit 104ton/kwh；CAijkAvailable power generation capacity for i period j area k electricity power enterprises；
(6) power supply and demand balance constrains
DMijTotal electricity demand for the j area planning phases
(7) region CO2 emission constrains
ERkFor i period j regional power system CO2The discharge capacity upper limit
QjFor the voluntary emission reduction of authentication in the j area planning phases, unit 104ton；
(8) nonnegativity restrictions
APijk≥BQijk≥0 (1f)。
Electricity needs is to influence the important uncertain factor of China's CO2 emission.Under conditions of steady growth, one The power demand in a area is still uncertain, possible time to time change, can be considered as a stochastic variable, when Before this stochastic variable is realized, need to plan the power generation level allowed in region.Stochastic programming can be used for It solves to contain stochastic variable in those constraintss, and the optimization problem that must be made a policy before stochastic variable realization. Stochastic variable pre-conversion processing is several certainty scenes by many traditional planning methods, then separately verifies each solution side Case is under these certainty scenes to the satisfaction degree of model constraint.Once the constraint that programme is unsatisfactory for regulation scene will When asking, it will be divided into infeasible scheme and be given up.Therefore, such constraint actually " rigid constraint ", is thus got most Excellent scheme is to need to pay " cost " in some aspects, can not be well adapted for the need of practical power generation programmed decision-making It will.Using different treatment principles in the present invention, permission is made decision is unsatisfactory for constraints in some cases, but needs The probability that constraints is set up is not less than specific confidence level.This is because for generating equipment, it is full for a long time to send out, is super The risk of unit contingency is bigger caused by hair.Under normal conditions, the generated energy of generating set can pass through installed capacity It is weighed.However, installed capacity is a fully loaded gross data, the power of generating set is usually not fully utilized, because This, first can set power generation capacity-constrained according to mean power using level.This numerical value will be generally less than installed capacity (such as capacity factor measure is 93%).On the other hand, when coping with power load peak, unit is but needed completely to send out even super generating sometimes User demand could be met reluctantly, but the service life of equipment can be influenced in this way, once contingency occurs for unit, can also be given Electric power netting safe running brings risk.Therefore, although permission is made decision in certain special cases, (there is electric power in peak of power consumption During notch) constraints is unsatisfactory for, but the probability for the condition establishment that needs restraint is not less than specific confidence water.Therefore, it is reply During summer peak meeting, high temperature, high humidity are met when electric power notch occur in extreme weather conditions, needs that power balance prediction scheme is taken to arrange It applies, for different notch grades, the electricity generating plan for allowing risk of selection probability different.This will be helpful to policymaker according to separated The anti-possibility constrained is made decision.Therefore, equipment can be exceeded to nominal output ability to greatest extent using chance constraint And caused two uncertain factors of risk are included in planning, and can also by the constraints of model in optimization process by " rigidity meets " switchs to appropriate " flexibly response ", therefore gained scheme can have both flexibly while optimization aim is met Property and operability, while ensureing the safe operation of electric system, improve programme implementation result science and it is credible Degree.
On the basis of chance constraint, using two stage stochastic programming method, when uncertainty shows as the thing of randomness After part occurs, recourse mechanism can be taken to reduce the influence that chance event is brought.Traditional low-carbon technology research In, two-phase method is often applied individually to any Input-Output Efficiency Analysis, to study the decision of enterprise's low-carbon Innovation Input and yield, And influence of the Technical Overflow effect to manufacturing enterprise's low-carbon technology model selection of innovation.Combine the two benches of chance constraint Stochastic programming compensates for the deviation generated when model is used alone, obtains the analysis result with realistic meaning, be more suitable for Complicated electricity power enterprise.
In addition to this, coal consumption, transaction value, emission reduction cost etc. have programme during low-carbon electric power production programming Great influence, and these parameters for influencing planning equally have uncertainty, and its probability distribution and membership function are not easy It obtains or data volume is huge.Many traditional treatment methods while facilitating planning, can but be neglected using history average as parameter Having omited influences caused by the uncertainty of these parameters.Therefore, this patent is used is processed into section by these uncertain informations The method of form by the professional standing and experience of expert and stakeholder, is put parameter as known section bound but is not known The interval parameter of its probability distribution not only reduces the requirement of data volume, and to that can not obtain variable probability distribution and be subordinate to When spending function, the uncertainty of coefficient in object function and constraint can be effectively solved, obtains stablizing feasible interval solutions.Separately On the one hand, decision objective is typically an economic goal (cost minimization or profit is maximum), however, best economic goal It is possible that certain negative effect is brought to the energy and environment.Therefore it provides best optimal case based on economic goal and most Poor optimal case section allows policymaker the value of decision variable be adjusted in section, according to actual conditions so as to be expired The programme of meaning provides scientific basis for decision support.But when section is excessive, infeasible solution can be generated or due to solution Range it is too big and lose decision meaning.Therefore, by Interval Programming combination chance constraint, stochastic programming, two benches planning etc. no Deterministic optimization method handles complication system planning problem Feasible degree and implementation result more preferably together.
The influence that the transaction of scenario analysis carbon emission is divided to generate power generation planning, it is a kind of reasonable that discharge capacity is converted into Cost, it by active balance profit-push it is maximized during cost of implementation control and combustion with minimal target.The opposing party Face, the operational management of energy internet focus on conglomerate collaboration Optimum Regulation, and enterprise income is converted into CO by model2Year generates Corresponding income is measured, carbon emission quota is facilitated to flow and reconfigure between different industries enterprise in the future, it is thus possible to more It is effective to play most optimum distribution of resources effect.
To sum up, medium-term and long-term energy policy appraisal include prediction, balance and optimization three aspect contents, need to compared with In long time scale and make a response with very big probabilistic variation, this not only needs the most probable energy that looks to the future Source development trend will more be studied and change the required precondition of this various possibilities of trend possibility different with realization.It builds Vertical mixing low-carbon electric power model, model is in different project periods, region power generation net profit is up to object function.Simultaneous While caring for electric power enterprise profit, the control of total carbon emission is realized.The CO2 emission generated by power generation in region Amount is uncertain (being expressed as a stochastic variable), it is necessary to formulate generation schedule before the value for obtaining the stochastic variable.
Specific calculating process includes：
It will be in object function
phFor the probability level of certain electricity needs, h is electricity needs level.
Object function becomes
Constraints is
Pr[{GCijk≤CAijk}]≥1-qn (2)
APijk≥BQijk≥0 (2f)
qnRepresent that violating this constrains permitted probability.
Secondly, can chance constraint be converted into deterministic and linear constraint (i) for about in the following way Beam n determines a probability level qn(ii) so that constraints n is at least in 1-qnOn meet constraints.Therefore, constraints (1) become：
(1)
For inverse cumulative distribution function
(2) each DMijDistribution can be converted into the set of the centrifugal pump of equal value with it.Allow each DMijTake Probability ph's Value Wh.Therefore constraints (2c) is converted into：
APijk≥BQijkh≥0 (3f)
The uncertainties of the parameters such as carbon transaction price, emission reduction cost, model is represented with interval number, can effectively be solved The uncertainty of coefficient, obtains stablizing feasible interval solutions in object function and constraint.Model after reconstruct is as follows：
APijk ±≥BQijkh ±≥0 (4f)
It is planned according to two-step method come solution interval.It is as follows corresponding to the submodel of the upper limit
APijk +≥BQijkh +≥0 (5f)
The submodel corresponding to lower limit is solved again
APijk -≥BQijkh -≥0 (6f)
Plan model is applied to the power planning under carbon transaction city carbon quota difference distribution off field situation, respectively at certain 3 electricity power enterprises similar in region selection A, B Liang Ge cities scale, each emission reduction is horizontal different, studies it in t1, t2, t3 Power generation situation in project period.Carbon transaction pilot provinces and cities exempt from each enterprise implement for being included in carbon emission trade system at present Take and provide quota system.Each enterprise's year initial quota is shown in Table 1.Second of distribution situation is by taking free quota drops to 50% as an example. CO2Transaction value can also change with the reduction of free quota, referring to table 2.Each enterprise's emission reduction cost is shown in Table 3.Model In emission reduction efficiency, according to industry average level estimate.Target generated energy is referring to table 4.Each current coal consumption situation of enterprise is referring to table 5.S1 is the situation of quota Free distribution；S2 is the situation that free quota falls to 50%.
1 each enterprise CO of table2Annual initial quota
2 each enterprise of table buys CO2Quota cost
3 each enterprise's emission reduction cost of table
4 target generated energy of table
5 each enterprise's coal consumption of table
City K=1 K=2 K=3
J=A 0.458 0.356 0.626
J=B 0.404 0.312 0.591
Increase CO caused by power generation under 6 S1 situations of table newly2Amount
When table 6 is that electricity needs changes under S1 situations, produced more than power generation is increased after first stage target generated energy newly Raw CO2 amounts.
As can be seen from the table, wherein second, third project period of CO2 total amounts are compared with having decline, the original of decline the first project period Because other than each enterprise's emission reduction efficiency improves this factor, regenerative resource enterprise being encouraged to pass through Chinese core in carbon transaction market It is another important factor in order that resource emission reduction, which is demonstrate,proved, for merchandising.Regenerative resource is by the implementation of carbon transaction in energy supply It will be accessed in net with higher ratio, and preferably solve electric power growth and decline this contradiction with carbon emission amount.
The scheme that is provided by model is it is found that three kinds of electricity needs levels Liang Ge enterprise (enterprise in A city minimum to coal consumption Industry 2 and the enterprise 2 in B cities) influence it is little, show as this Liang Jia enterprise three kinds of electricity in same project period, same risk level New increment life insurance is held essentially constant and (generates stable carbon emission) under power desired level, and the shared market share is stablized.Such as, During one project period, the enterprise 2 in A cities is in Pn=0.01 under high, medium and low three kinds of level of power, and value is all [21.1,23.0], Pn All for [14.4,16.3] when=0.05, when Pn=0.1 is all [11.5,12.5]；The enterprise 2 in B cities is all in Pn=0.01 [20.2,22.1] are all [14.4,15.4] during Pn=0.05, and when Pn=0.1 is all [10.6,11.5].That is, not Same project period, when electricity needs level changes, the impacted minimum of institute of this Liang Jia enterprise.(B cities of the minimum enterprise of coal consumption Enterprise 2) it is not all impacted in different project periods, risk and electricity needs, have stable power generation to it Demand, the shared market share are stablized.This illustrates that economic and environment-friendly power plant puts more effort in emission reduction, electricity needs variation, wind In the case of indefinite various of dangerous factor, stable development can be still kept, forms the benign cycle of sustainable development.What model provided Decision scheme, can excite enterprise's adjustment structure and the enthusiasm of technological transformation, and guiding encourages high energy-consuming enterprises to be set from enterprise Standby, technique, management etc. are actively changed, and are met the market requirement, and then are reached and further total carbon emission is promoted to decline Target.
Fig. 1 is under S1 situations, when interval parameter is set as the upper limit, different risks, different electricity needs and project period the Two stage optimization electricity generating plan.
Prioritization scheme as obtained by model solution shows under S1 situations, though initial stage quota of discharge is free, but with The increase of emission reduction dynamics, in second third project period, generate electricity the power plant that will turn to more economical environmental protection, and plan model, which will exceed, to be counted The electricity needs drawn preferentially distributes to four relatively low enterprises of coal consumption, the respectively enterprise 1 in A cities and enterprise 2, the enterprise 1 in B cities With enterprise 2.In contrast, the highest Liang Jia enterprises of coal consumption only have in middle and high electricity needs level a small amount of distribution even without Distribution, this Liang Jia enterprise is respectively the enterprise 3 in A cities and the enterprise 3 in B cities.National carbon emission trade system will started and subtracted Under the dual background that row's dynamics continues to increase, the enterprise of opposite pollution will be gradually by market.
The enterprise 2 in the B city minimum for coal consumption of the enterprise of impacted minimum, in different times, different risks, different electric power It is preferably that it distributes additional issue demand under demand situation.This illustrates that economic and environment-friendly power plant puts more effort in emission reduction, and electric power needs It asks variation, in the case of indefinite various of risk factors, can still keep stable development, form the benign cycle of sustainable development.Mould Type can recognize the optimal solution of the power generation under different carbon emission allocation plans, so as to reach allocation of quota in region, electric power Balance between production and economic goal.
As can be seen from Figure 2, when free quota drops to 50%, only the minimum two electric power enterprises institute of coal consumption is impacted Minimum when there is additional issue demand, has stable share to be arranged to this Liang Jia enterprises (the respectively enterprise 2 in A cities, B city always Enterprise 2)；And the impacted maximum of (the respectively enterprise 3 in A cities, the enterprise 3 in B cities) institute of the highest Liang Jia enterprises of coal consumption, each A period is hardly assigned any additional issue amount.
Fig. 3 is illustrated under S2 situations, when electricity needs is medium level, the CO of 3 project periods2Yield.It can by figure See, ensureing target generated energy under the premise of each steady growth project period 5 percent, CO2 emissions are in decline Trend.It can be seen that model can be adapted for the power planning problem under carbon transaction market mechanism, and electricity needs can met Realize emission reduction targets simultaneously.
Fig. 4 is two city of A, B in different pnProfit lower limit under level during different situations.As can be seen that B cities are when each The gross profit of phase is above A cities, this is because the coal consumption of 3 enterprises of B cities is lower, energy conservation and environmental protection higher level.In addition, A, B two The net benefits in city with free quota proportion decline (under S2 situations) and decline (with Pn=0.01, for period=2, A cities Drop to 226.3 from 254.8；277.1) B cities drop to from 306.3.Therefore, electric power enterprise should cope with carbon friendship from many aspects The influence that the implementation in easy market brings enterprise, increases the input of energy-conserving and emission-cutting technology and equipment, actively improves energy efficiency, Optimization of Energy Structure.
This embodiment is merely preferred embodiments of the present invention, but protection scope of the present invention is not limited thereto, Any one skilled in the art in the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in, It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims Subject to.

Claims (4)

1. a kind of uncertain two benches chance constraint low-carbon electric power Method for optimized planning, including：
According to the history and prediction data obtained in scheduling system in intelligent grid and load prediction system, and with reference to energy-saving and emission-reduction Index and carbon emission transaction value data, using profit maximization as target, establish low-carbon electric power plan model；Using chance about Electricity needs, carbon emission quota, super generating risk are included in planning by beam, and use two stage stochastic programming method, and optimization establishes two Stage chance constraint low-carbon electric power plan model will have probabilistic parameter setting into range format, obtain steady in model Fixed feasible interval solutions.
2. method according to claim 1, which is characterized in that the profit computational methods are：Carbon dioxide is expected yield Corresponding income subtracts cost produced by reducing emission of carbon dioxide plus the income corresponding to the super expected yield of carbon dioxide, then The CO2 emission transaction cost beyond free carbon emission quota is subtracted, if the dioxy beyond free carbon emission quota It is negative to change carbon emission transaction cost, then shows also extra carbon emission power for merchandising to be converted to profit.
3. method according to claim 1, which is characterized in that the chance constraint includes：Generated energy and available power generation capacity Between total amount constraint, the power supply and demand balance constraint between generated energy and electricity needs, CO2 emissions constraint, power generation The nonnegativity restrictions of amount and electricity needs.
4. method according to claim 1, which is characterized in that the two benches chance constraint low-carbon electric power plan model packet It includes：
Maxf=max (f1+f2-f3-f4) (1a)
Wherein, f represents profit, f1Represent that carbon dioxide is expected the income corresponding to yield, f2Represent the super expected production of carbon dioxide The corresponding income of raw amount, f3Cost produced by representing reducing emission of carbon dioxide, f4Represent the titanium dioxide beyond free carbon emission quota Carbon emission transaction cost；I=1,2,3 ..., I expression period sequence, j=1,2,3 ..., J expression ground region sequence, k=1,2, 3 ..., K represent electricity power enterprise's sequence, APijkExpection carbon dioxide year yield for i period j area k electricity power enterprises is used as First stage decision variable, CIijkIncome of the unit carbon dioxide year corresponding to yield for i period j area k electricity power enterprises, E [] is expectation of a random variable；BQijkElectricity needs to work as i periods j area is DMijWhen, k electricity power enterprises are more than APijk Carbon dioxide yield is used as second stage decision variable caused by power generation；GQijk=APijk+BQijkFor i period j area k The total yield of electricity power enterprise's carbon dioxide；ηijkEmission reduction efficiency for i period j area k electricity power enterprises；RCijkIt is sent out for i periods j city k The unit carbon dioxide emission reduction cost of electric enterprise；EAijkThe free carbon emission quota provided in advance for i period j area k electricity power enterprises； BPijkThe transaction value of right to emit carbon dioxide is bought for i period j area k electricity power enterprises；
Constraints is：
(1) total amount constrains
GCijk≤CAijk (1b)
GCijk=(GQijk/(DSijkEL)) (1b_1)
GCijkGenerated energy for i period j area k electricity power enterprises；DSijkFor unit generated energy coal consumption；EL is CO2 emission system Number, unit 104ton/kwh；CAijkAvailable power generation capacity for i period j area k electricity power enterprises；
(2) power supply and demand balance constrains
DMijTotal electricity demand for the j area planning phases
(3) region CO2 emission constrains
ERkFor i period j regional power system CO2The discharge capacity upper limit
QjFor the voluntary emission reduction of authentication in the j area planning phases, unit 104ton；
(4) nonnegativity restrictions
APijk≥BQijk≥0 (1f)。
CN201711379937.4A 2017-12-20 2017-12-20 Uncertain two benches chance constraint low-carbon electric power Method for optimized planning Pending CN108268973A (en)

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