CN109038553A - A kind of section random basis possibility planing method under condition of uncertainty - Google Patents
A kind of section random basis possibility planing method under condition of uncertainty Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The invention discloses the section random basis possibility planing methods under a kind of condition of uncertainty, it include: the general requirement planned according to urban energy system, determine each uncertain parameters, urban energy system plan model under condition of uncertainty is established using section random basis possibility planing method, and certainty linear optimization problem is converted for uncertain nonlinear optimal problem according to simplex method, calculate the decision variable result under different brackets level, finally obtain the optimal energy supply structure of urban energy system, pollutant discharge amount and the smallest system investment cost.The present invention has coupled Interval Programming, basic possibility planning and two stage stochastic programming, the multiple uncertainty being able to reflect in energy resource system, and the obtained energy supply structure of optimization, it can adapt to all possible planning scenes under energy resource system condition of uncertainty, there is very strong practicability to be convenient for Project Realization.
Description
Technical field
The invention belongs to dispatching automation of electric power systems technical fields, and in particular to the area under a kind of condition of uncertainty
Between random basis possibility planing method.
Background technique
Since fossil energy is cheap compared to the non-fossil energy, most power generation mode still will rely on fossil energy.
2015, Chinese fossil energy generated energy accounted for the 75.4% of total power generation, also, will also constantly increase in following decades
Add.However fossil energy is limited and non-renewable, the exploitation of fossil energy and using also resulting in a series of pollution
Problem, for example, the discharge amount of annual carbon dioxide has 85% from fossil energy, a large amount of greenhouse gas emission accelerates temperature
The generation of room effect.Brought problem of environmental pollution has become the whole world pass when electric power energy supply and demand safety and power generation
The topic of note.It makes prediction, then distinguishes firstly the need of by electric power energy demand amount of the effective method to terminal user at present
Each structure (production, conversion, transmission, distribution and use) link of electric system is made and being made rational planning for.However electric power energy
System structure is huge, and relationship is intricate between subsystem, complexity and uncertainty with higher, and these subsystems
There is biggish correlation between system and disposal of pollutants.Therefore, effectively reflect and solve the complexity in energy resource system and not
Certainty, the uncertain correlation analysed in depth between tradeoff each section is the key that energy system planning.
Energy resource system is one and includes various probabilistic integrated systems, considers three main compositions in energy resource system
Part: (1) energy supply part provides different energy availabilities, including fossil energy (coal, oil and natural gas) and
Renewable energy (biomass, water power, wind-force etc.)；(2) energy conversion part, wherein comprising it is various with economy, technical solution,
The relevant electric energy conversion such as environment and technical performance；(3) electricity needs part, the technology including various demand sides, by big
The terminal user of the features such as amount different society economy, geography, population, technological progress and environmental condition pushes the consumption of the energy.
Uncertainty is mainly reflected in the following aspects: (1) parameter uncertainty (such as cost of electricity-generating and generation technology data
Deng)；(2) operating status is uncertain (such as booting, shutdown or unit failure)；(3) data uncertainty is (such as economic or skill
Art data etc.)；(4) uncertainty in traffic (prediction of such as power demand or peakload).Probabilistic source is main
It is embodied in the following aspects: (1) internal factor, the i.e. unstability of power energy system equipment；(2) external factor, day
The various meteorologic factors such as gas, temperature；(3) information uncertainty, as electric system continues to develop, information uncertainty is increasingly
Enhancing；(4) subjective uncertainty, since experience and the ability difference of policymaker cause to plan that uncertainty occurs in effect.Cause
This, it is effective to develop uncertainty optimization method to solve real energy resource system case be vital.
In order to preferably handle the uncertainty and complexity in power energy system, a large amount of researcher is developed
A series of scientific and effective optimization methods, including Interval Programming, fuzzy programming and stochastic programming and many prognosis modellings and excellent
Change method, and successfully these methods are applied in real case.However, existing uncertain method seldom considers
Randomness during electric power energy correlated activation, and for the research of electric system risk analysis problem be even more it is few again
It is few.Also, single uncertain method not can solve multiple uncertainty, can do nothing to help policymaker's analysis relevant policies and wants
It asks, limitation does not have any guarantee to problem analysis.Simply by different planing method combined treatments, still can not solve
The certainly complexity and uncertainty in power energy system needs to innovate conventional method and improved, it is allowed more to approach
True system.Therefore, for China's urban electric power energy characteristics, carry out uncertain simulation and optimizing research method, to electricity
Force system is analyzed comprehensively, provides scientific, effective, reliable theoretical foundation for China's electric system.
Existing uncertainty planing method can not the scientific and effective uncertainty solved in electric system, and it is single
One method not can solve multiple uncertainty, can do nothing to help policymaker and analyzes relevant policies requirement, limitation asks analysis
Topic does not have any guarantee, thus be badly in need of it is a kind of can overcome the various complexity faced in Electric Power Network Planning in the prior art and
Multiple uncertainty.
Summary of the invention
In order to solve the problems, such as to mention in background technique, the invention discloses the section under a kind of condition of uncertainty with
Machine basis possibility planing method, which comprises the following steps:
Step 1 determines each uncertain parameter in energy resource system:
According to the general requirement that urban energy system is planned, complexity and uncertainty in energy resource system are analyzed, is determined
Each uncertain parameters in urban energy system Optimized model, and obtain the fluctuating change range of each uncertain parameters；Its
Middle economic data, technical data and pollutant related data indicate with smeared out boundary interval parameter, power demand, motor-driven
Vehicle owning amount indicates that other traffic datas and electrical power conversion data are indicated with interval number with stochastic variable；Uncertain parameter packet
Include electricity needs, vehicle ownership, demand for energy；
Step 2, the following distribution for establishing uncertain parameters in energy resource system:
According to the average growth rate per annum of power consumption over the years and vehicle ownership, their own distribution pattern is determined,
And multiple Monte Carlo simulation is carried out, it is fitted their probability distribution curve, and determined under different brackets level on this basis
Future electrical energy demand and vehicle ownership；
Step 3 constructs uncertain urban energy system Optimized model:
It is each according to being obtained in the uncertainty description step 1 and step 2 in the random basis possibility planing method of section
The fluctuating change range of uncertain parameter, and urban energy system plan model is combined, construct range of indeterminacy energy resource system
Optimized model；
Step 4 solves uncertain urban energy system Optimized model:
Using simplex method and section random basis possibility planing method, the fluctuation for calculating each uncertain parameter becomes
Change the corresponding energy supply structure of each occurrence, pollutant discharge amount and the smallest system investment cost in range,
Generate the energy source optimization database for adapting to each uncertain parameters within the scope of fluctuating change.
In the step 1, each uncertain parameters under urban energy system model include not true inside energy resource system
Qualitative and energy system planning uncertainty, wherein the uncertainty inside energy resource system includes the uncertainty of parameter, fortune
The uncertainty of the uncertainty of the uncertainty of row state, the uncertainty of measurement and prediction, energy system planning includes
Historical data is insufficient, randomness, the uncertainty of prediction data itself and the subjective differences of data processing of data acquisition；
In the step 2, the stochastic variable in each Monte Carlo simulation is both configured to multiple.
The step 3 is divided for following steps:
Step 31, in conjunction with each uncertain parameter obtained in step 1 and step 2 fluctuating change range while, consider
Urban energy system plan model.
Step 32, building Regional Energy system optimization model, using the planning of section random basis possibility and the practical energy
The energy resource system model of system combines, and according to average annual electricity needs growth rate distribution curve, considers power supply and demand balance about
The various constraint conditions such as beam, equality constraint, variable active cost constraint and dilatation selection constraint, if the power demand analogue value
For d, Regional Energy system optimization model are as follows:
The objective function of Regional Energy system optimization model are as follows:
Minz=cx+fy (1)
Each constraint condition:
Power supply and demand balance constraint are as follows:
Ax≥d (2)
Equality constraint are as follows:
Bx=0 (3)
Power plant's units limits are as follows:
Sx≤Ny (4)
Dilatation selection constraint are as follows:
Ty≤1 (5)
y∈{0,1},x≥0 (6)
Wherein: x represents continuous variable, and y represents 0-1 variable, and f represents fixed cost, and c represents variable cost, A, B, S, T generation
Table model parameter, N represent dilatation scale；
Step 33 introduces section random basis possibility planing method, and setting power consumption d is fuzzy random variable,
The Regional Energy system optimization model established to step 2 carries out formula conversion, converts certainty for uncertain optimization problem
Optimization problem, after conversion formed conversion energy system optimization:
Convert the objective function of energy system optimization are as follows:
Constraint condition are as follows:
Power supply and demand balance constraint are as follows:
Equality constraint are as follows:
Power plant's units limits are as follows:
Dilatation selection constraint are as follows:
T±y≤1 (12)
Nonnegativity restrictions are as follows:
Wherein, n, r, behalf positive integer, α and β are the Truncated set level of the function of fuzzy membership, ± it is section number,For
The left margin in smeared out boundary section,For the right margin in smeared out boundary section,WithRepresent determining for the first and second stages
Plan variable；Represent the stochastic variable under different probability level, pjhRepresent probability level.
The planning process of urban energy system plan model is to consider eight kinds of electrical power conversion facilities and five kinds of terminal users
Electricity needs, objective function is divided into four parts, respectively Electricity Investment cost, investment in transportation cost, risk punishment,
Pollutant catabolic gene cost；According to average annual electricity needs growth rate, it is fitted electricity needs growth rate distribution curve, considers that quality is flat
These four constraint conditions after weighing apparatus constraint, power supply and demand balance constraint, environmental constraints and other nonnegativity restrictions, optimize region energy
Energy supply structure in the system of source, Regional Energy power consumption is definite value a few days ago, considers the influence of uncertain factor,
The power demand of optimization reflects the energy resource structure variation under different brackets level to be from low to high four hierarchical levels.
The beneficial effects of the present invention are:
1) according to the average growth rate per annum of power consumption over the years and vehicle ownership, their own distributional class is determined
Type, and multiple Monte Carlo simulation is carried out, it is fitted their probability distribution curve, and determine different brackets water on this basis
Future electrical energy demand and vehicle ownership under flat can evade and lack random process in electric system to electric power energy
It is influenced caused by correlated activation, to keep smart grid scheduling more reliable；
2) energy supply structure that the present invention acquires is using section random basis possibility planing method it is emphasised that hardness
Constraint, for each possible situation in uncertain set, obtained optimal energy supply structure can meet all
Constraint condition, the result of the model optimization remains feasibility, and the model can clearly obtain different brackets water
Supply situation and electric power import situation inside electric power under flat, according to first stage preset each power plants generating electricity target, more
Evade the system risk as brought by subjective decision well, to ensure safe operation of power system.
3) present invention has coupled Interval Programming, possibility planning and two stage stochastic programming, is capable of handling energy resource system
In it is multiple uncertain and reflect influence of the risk analysis to electric system, and the energy supply structure that optimization obtains can
All possible planning scenes under energy resource system condition of uncertainty are adapted to, there is very strong practicability, be convenient for Project Realization.
Detailed description of the invention
Fig. 1 is the urban energy system plan model flow chart under the condition of uncertainty of building；
Fig. 2 is the probability density function of the average growth rate per annum of power demand and vehicle ownership；
Fig. 3 is the power supply structure under different brackets level.
Specific embodiment
The present invention will be further described in the following with reference to the drawings and specific embodiments.
The present embodiment is to certain urban energy system optimization process, as shown in Figure 1, the condition of uncertainty of the present embodiment
Under m- random-basic possibility planing method in coupled zone urban energy system model, specifically includes the following steps:
Step 1 determines each uncertain parameter in energy resource system
According to the general requirement that urban energy system is planned, complexity and uncertainty in energy resource system are analyzed, is determined
Each uncertain parameters in urban energy system Optimized model, and obtain the fluctuating change range of each uncertain parameters；Its
Middle economic data, technical data and pollutant related data indicate with smeared out boundary interval parameter, power demand, motor-driven
Vehicle owning amount indicates that other traffic datas and electrical power conversion data are indicated with interval number with stochastic variable；Uncertain parameter packet
Include electricity needs, vehicle ownership, demand for energy etc.；
In step 1, each uncertain parameters under urban energy system model include uncertain inside energy resource system
Property and energy system planning uncertainty, wherein the uncertainty inside energy resource system include parameter uncertainty, operation
The uncertainty of the uncertainty of state, the uncertainty of measurement and prediction, the uncertainty of energy system planning include going through
History data deficiencies, randomness, the uncertainty of prediction data itself and the subjective differences of data processing of data acquisition；
Step 2, the following distribution for establishing uncertain parameters in energy resource system,
According to the average growth rate per annum of power consumption over the years and vehicle ownership, their own distribution pattern is determined,
And multiple Monte Carlo simulation is carried out, it is fitted their probability distribution curve, and determined under different brackets level on this basis
Future electrical energy demand and vehicle ownership (as shown in Figure 2)；
In step 2, the stochastic variable in each Monte Carlo simulation is both configured to multiple.
Step 3 constructs uncertain urban energy system Optimized model
It is each according to being obtained in the uncertainty description step 1 and step 2 in the random basis possibility planing method of section
The fluctuating change range of uncertain parameter, and urban energy system plan model is combined, construct range of indeterminacy energy resource system
Optimized model；This step specifically:
Step 31, in conjunction with each uncertain parameter obtained in step 1 and step 2 fluctuating change range while, consider
Urban energy system plan model.
The planning process of urban energy system plan model is to consider eight kinds of electrical power conversion facilities (fire coal hair in this step
Electricity, fuel gas generation, wind-power electricity generation, hydroelectric generation, biomass power generation, garbage power, photovoltaic power generation and pumped-storage power generation) and
The electricity needs of five kinds of terminal users's (primary industry, industry, resident, construction industry, tertiary industry), is divided into four for objective function
A part, respectively Electricity Investment cost, investment in transportation cost, risk punishment, pollutant catabolic gene cost；According to average annual electric power
The growth rate of demand is fitted electricity needs growth rate distribution curve, considers mass balance constraint, power supply and demand balance constraint, environment
Constraint and other nonnegativity restrictions after these four constraint conditions, optimize Regional Energy system in energy supply structure, day proparea
Domain electricity power consumption figure is definite value, considers the influence of uncertain factor, the power demand of optimization be from low to high be four
A hierarchical level reflects the energy resource structure variation under different brackets level；
Step 32, building Regional Energy system optimization model, using the planning of section random basis possibility and the practical energy
The energy resource system model of system combines, and according to average annual electricity needs growth rate distribution curve, considers power supply and demand balance about
The various constraint conditions such as beam, equality constraint, variable active cost constraint and dilatation selection constraint, if the power demand analogue value
For d, Regional Energy system optimization model are as follows:
The objective function of Regional Energy system optimization model are as follows:
Min z=cx+fy (1)
Each constraint condition:
Power supply and demand balance constraint are as follows:
Ax≥d (2)
Equality constraint are as follows:
Bx=0 (3)
Variable active cost constraint are as follows:
Sx≤Ny (4)
Dilatation selection constraint are as follows:
Ty≤1 (5)
y∈{0,1},x≥0 (6)
Wherein: x represents continuous variable, and y represents 0-1 variable, and f represents fixed cost, and c represents variable cost, and d represents electricity
Power demand, A, B, S, T representative model parameter, N represent dilatation scale.
Step 33 introduces section random basis possibility planing method, and setting power consumption d is fuzzy random variable,
The Regional Energy system optimization model established to step 2 carries out formula conversion, converts certainty for uncertain optimization problem
Optimization problem, after conversion formed conversion energy system optimization:
Convert the objective function of energy system optimization:
Constraint condition are as follows:
Power supply and demand balance constraint are as follows:
Equality constraint are as follows:
Power plant's units limits are as follows:
Dilatation selection constraint are as follows:
T±y≤1 (12)
Nonnegativity restrictions are as follows:
Wherein, n, r, behalf positive integer, α and β are the Truncated set level of the function of fuzzy membership, ± it is section number,For
The left margin in smeared out boundary section,For the right margin in smeared out boundary section,WithRepresent determining for the first and second stages
Plan variable；Represent the stochastic variable under different probability level, pjhRepresent probability level；
In this conversion energy system optimization, power demandConsider different Truncated set levels, different probability water
All possible electricity needs situation under flat, combined result still maintain feasibility, therefore are that reliably, can sufficiently fit
Answer each probabilistic possible case；
Step 4 solves uncertain urban energy system Optimized model
Using simplex method and section random basis possibility planing method, the fluctuation for calculating each uncertain parameter becomes
Change the corresponding energy supply structure of each occurrence, pollutant discharge amount and the smallest system investment cost in range,
Generate the energy source optimization database for adapting to each uncertain parameters within the scope of fluctuating change.
As shown in Fig. 2, the probability density function of the average growth rate per annum of the present embodiment power demand and vehicle ownership
In, basic, normal, high, high four hierarchy levels are in turn divided into, reflect electricity needs under different brackets level and motor-driven
Vehicle owning amount situation can evade shortage random process influence caused by electric power energy correlated activation in electric system.
As shown in figure 3, in optimal power supply structure under four kinds of hierarchy levels of the present embodiment: at a low level, rule
In the phase of drawing import electricity account for the ratio of total power generation be more than 50% (the 1st period accounting 54.3%, the 2nd period accounting 60.8%,
3rd period accounting 61.3%, the 4th period accounting 61.8%, the 5th period accounting 62.1%)；It is high it is horizontal under, each period into
It is respectively 58.1%, 67.0%, 70.1%, 73.0% and 75.4% that mouth electricity, which accounts for total power generation ratio,.The model can understand
Ground obtains supply situation and electric power import situation inside the electric power under different brackets level, preset each according to the first stage
A power plants generating electricity target preferably evades the system risk as brought by subjective decision, ensures safe operation of power system.
Claims (5)
1. the section random basis possibility planing method under a kind of condition of uncertainty, which comprises the following steps:
Step 1 determines each uncertain parameter in energy resource system:
According to the general requirement that urban energy system is planned, complexity and uncertainty in energy resource system are analyzed, determines city
Each uncertain parameters in energy system optimization, and obtain the fluctuating change range of each uncertain parameters；Wherein pass through
Ji data, technical data and pollutant related data indicate that power demand, motor vehicle possess with smeared out boundary interval parameter
Amount indicates that other traffic datas and electrical power conversion data are indicated with interval number with stochastic variable；Uncertain parameter includes electric power
Demand, vehicle ownership, demand for energy；
Step 2, the following distribution for establishing uncertain parameters in energy resource system:
According to the average growth rate per annum of power consumption over the years and vehicle ownership, determines their own distribution pattern, go forward side by side
The multiple Monte Carlo simulation of row, is fitted their probability distribution curve, and determines under different brackets level on this basis not
Come power demand and vehicle ownership；
Step 3 constructs uncertain urban energy system Optimized model:
It is each not true according to what is obtained in the uncertainty description step 1 and step 2 in the random basis possibility planing method of section
Determine the fluctuating change range of parameter, and combines urban energy system plan model, building range of indeterminacy energy resource system optimization
Model；
Step 4 solves uncertain urban energy system Optimized model:
Using simplex method and section random basis possibility planing method, the fluctuating change range of each uncertain parameter is calculated
The interior corresponding energy supply structure of each occurrence, pollutant discharge amount and the smallest system investment cost generates suitable
Answer energy source optimization database of each uncertain parameters within the scope of fluctuating change.
2. the section random basis possibility planing method under a kind of condition of uncertainty according to claim 1, special
Sign is, in the step 1, each uncertain parameters under urban energy system model include uncertain inside energy resource system
Property and energy system planning uncertainty, wherein the uncertainty inside energy resource system include parameter uncertainty, operation
The uncertainty of the uncertainty of state, the uncertainty of measurement and prediction, the uncertainty of energy system planning include going through
History data deficiencies, randomness, the uncertainty of prediction data itself and the subjective differences of data processing of data acquisition.
3. the section random basis possibility planing method under a kind of condition of uncertainty according to claim 1, special
Sign is, in the step 2, the stochastic variable in each Monte Carlo simulation is both configured to multiple.
4. the section random basis possibility planing method under a kind of condition of uncertainty according to claim 1, special
Sign is that the step 3 is divided for following steps:
Step 31, in conjunction with each uncertain parameter obtained in step 1 and step 2 fluctuating change range while, consider city
Energy system planning model；
Step 32, building Regional Energy system optimization model, using the planning of section random basis possibility and practical energy resource system
Energy resource system model combine, according to average annual electricity needs growth rate distribution curve, consider power supply and demand balance constraint, equation
The various constraint conditions such as constraint, variable active cost constraint and dilatation selection constraint, if the power demand analogue value is d, region
Energy system optimization are as follows:
The objective function of Regional Energy system optimization model are as follows:
Minz=cx+fy (1)
Each constraint condition:
Power supply and demand balance constraint are as follows:
Ax≥d (2)
Equality constraint are as follows:
Bx=0 (3)
Power plant's units limits are as follows:
Sx≤Ny (4)
Dilatation selection constraint are as follows:
Ty≤1 (5)
y∈{0,1},x≥0 (6)
Wherein: x represents continuous variable；
Y represents 0-1 variable；
F represents fixed cost；
C represents variable cost；
A, B, S, T representative model parameter；
N represents dilatation scale；
Step 33 introduces section random basis possibility planing method, and setting power consumption d is fuzzy random variable, to step
The rapid 2 Regional Energy system optimization models established carry out formula conversion, convert uncertain optimization problem to deterministic excellent
Change problem forms conversion energy system optimization after conversion:
Convert the objective function of energy system optimization are as follows:
Constraint condition are as follows:
Power supply and demand balance constraint are as follows:
Equality constraint are as follows:
Power plant's units limits are as follows:
Dilatation selection constraint are as follows:
T±y≤1 (12)
Nonnegativity restrictions are as follows:
Wherein, n, r, behalf positive integer；H=1,2 ..., s；J=1,2 ..., n1；
α and β is the Truncated set level of the function of fuzzy membership；
± it is section number；
For the left margin in smeared out boundary section；
For the right margin in smeared out boundary section；
WithRepresent the decision variable in the first and second stages；
Represent the stochastic variable under different probability level；
pjhRepresent probability level.
5. the section random basis possibility planing method under a kind of condition of uncertainty according to claim 1 or 4,
It is characterized in that, the planning process of the urban energy system plan model are as follows:
Objective function is divided into four parts by the electricity needs for considering eight kinds of electrical power conversion facilities and five kinds of terminal users, respectively
For Electricity Investment cost, investment in transportation cost, risk punishment, pollutant catabolic gene cost；According to average annual electricity needs growth rate, intend
Electricity needs growth rate distribution curve is closed, mass balance constraint, power supply and demand balance constraint, environmental constraints is considered and other is non-
It breaks a promise these four constraint conditions after beam, the energy supply structure in Regional Energy system is optimized, wherein day forefoot area energy
Source power consumption is definite value, and considers the influence of uncertain factor；Power demand after optimization be from low to high be four
A hierarchical level.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101295875A (en) * | 2008-06-03 | 2008-10-29 | 山东电力研究院 | Method for establishing power transmission network planning model in uncertain surroundings |
| US20110276514A1 (en) * | 2010-05-04 | 2011-11-10 | International Business Machines Corporation | Evaluating the quality and risk-robustness of an energy generation capacity resource plan under inherent uncertainties in energy markets and carbon regulatory regime |
| CN103632205A (en) * | 2013-11-05 | 2014-03-12 | 常州大学 | Optimized electric-vehicle dispatching method considering wind-electricity and load uncertainty |
| CN105514988A (en) * | 2015-12-09 | 2016-04-20 | 何俊 | Micro power grid power supply planning scheme optimal selection method taking dynamic time-space features into consideration |
| CN106156955A (en) * | 2016-08-24 | 2016-11-23 | 南瑞（武汉）电气设备与工程能效测评中心 | A kind of consideration electric power demand side probabilistic power planning method |
| CN108073996A (en) * | 2018-01-29 | 2018-05-25 | 四川万益能源科技有限公司 | Urban energy panorama interactive mode big data platform management system and method |
-
2018
- 2018-07-31 CN CN201810855246.5A patent/CN109038553A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101295875A (en) * | 2008-06-03 | 2008-10-29 | 山东电力研究院 | Method for establishing power transmission network planning model in uncertain surroundings |
| US20110276514A1 (en) * | 2010-05-04 | 2011-11-10 | International Business Machines Corporation | Evaluating the quality and risk-robustness of an energy generation capacity resource plan under inherent uncertainties in energy markets and carbon regulatory regime |
| CN103632205A (en) * | 2013-11-05 | 2014-03-12 | 常州大学 | Optimized electric-vehicle dispatching method considering wind-electricity and load uncertainty |
| CN105514988A (en) * | 2015-12-09 | 2016-04-20 | 何俊 | Micro power grid power supply planning scheme optimal selection method taking dynamic time-space features into consideration |
| CN106156955A (en) * | 2016-08-24 | 2016-11-23 | 南瑞（武汉）电气设备与工程能效测评中心 | A kind of consideration electric power demand side probabilistic power planning method |
| CN108073996A (en) * | 2018-01-29 | 2018-05-25 | 四川万益能源科技有限公司 | Urban energy panorama interactive mode big data platform management system and method |
Non-Patent Citations (2)
| Title |
|---|
| 付殿峥: "不确定性优化技术在区域资源能源配置系统中的应用研究", 《中国博士学位论文全文数据库》 * |
| 董聪: "基于不确定性优化方法的能源环境系统规划模型研究", 《中国博士学位论文全文数据库》 * |
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