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Today, with advances that have occurred in electricity industry and technology of manufacturing all kinds of power plants whether renewable or perishable, making decision to choose the type and kind of an ideal plant is very important and strategic. For any weakness in determining short, medium and long term parameters affecting deciding whether technical, economical, environmental, social, political , and so on may cause irreparable damage. Also timing and fore sighting factors should be taken into account in decision-making equations. Selecting the type suitable for use in power plants connected to the network or independent sector is the main part of task. Therefore, because there are many variables and factors in the text and the margin of such a task, bed and plant kind selection is very difficult and time consuming. This choice is ultimately influenced by many technical and non-technical measures that are each divided into further subcategories. Due to repetition of this operation in the discussion of issues, finding an efficient way in this area would be very useful. In this paper, a hierarchical decision-making procedure for the selection of the ideal power for productivity and satisfaction in the operation of taking is introduced. That can be generalized to other types of construction and operation concepts in technologies of power plants.

If one or a series of investments believe that a power plant during its lifetime benefit for facilities will invest for facilities and devices of production [

The decision to invest in new power plant the investor should calculate all the long term marginal costs and the price of electricity in the plant may be sold to predict [

In selection of suitable power plants for the manufacture many factors are involved [

The next steps in parallel, technical and economic factors entered the field of elected, what type of technology and the capacity should be used to build power plants and that cover which amount of the initial investment and the rate of return on technology investments Technology of construction of primary energy consists of two categories:

A. Fossil fuel power plants or mortal

B. Renewable Power plants

That each spectrum has its own advantages, disadvantages and threats and must analysis any particular geographic location. Factors and other parameters exist that may at first glance to be less valuable than technical and economic factors that can confirm or deny construction of a power plant that we called all the effective factors in construction of a power plant in the first row of

The total cost of materials Equipment and setup. As well as maintenance and ground, Time: time to build, time startup or coming into orbit, useful life. Administrative: Office personnel to build, repair and maintenance. Environment: soil pollution, water, air, audio etc. Social and cultural rights: a positive impact on productivity, Political: sanctions and the arrival of new technologies, Cultural and natural heritage: damage, the area of land required, structural architecture: design art, finally, the tourism: a positive impact on an attraction. Although there are more details to mention that we betake to say them in this article, they are also listed in the first column of the table types of power plants. Within this table, we consider general and absolute technical and non-tech- nical specifications and feature a variety of power plants intended for construction. It should be noted that this table default and theoretical and complete expert system given the expertise is changeable and generalized. As you can see in this table numerical parameters are not faced with symmetric error but with a complex and heterogeneous asymmetric other highly nonlinear time-varying weights have been exposed to human factors were also strongly affected, So there is no escaping that we have to decide and choose the most efficient plant for the manufacture of any particular geographical area MADM techniques to take advantage of these issues [

In the construction and operation of all types of power plants due to exposure to various factors, there are many variables affecting the decision and the choice is difficult. In this selection there is technical and non-technical problem and they are often impossible to be collected [

Criteria Types of power plants | Geographical: (Influences of the place, Climate, Weather) | Technical | Economic | Time | Office (the number of personnel required) | Environmental: (water pollution. Soil. Air. Audio etc.) | Social and cultural (a positive impact on productivity) | Political (sanctions and in Advanced Technology | Cultural and natural heritage (Damage) | The area of required land | Architectural structures (artistic design) | Tourism (positive impact on attractions) | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Possessing technical knowledge | The complexity of the technology for power quality | Availability of fuel | Fuel prices | The total cost of materials. Equipment and installation | Time to build | Time startup or coming in orbit | Useful life | ||||||||||

1. Coal | Relatively dependent | Full | Low | Very very much | Very little | Low | Relatively little | avarage | much | average | Very much | Very little | Very little | Relatively much | Relatively average | Very low | Very little |

2. Gaseous | Relatively dependent | Full | Low | Very much | little | Low | litlle | little | average | Relatively low | little | little | Very little | little | Relatively low | low | Very little |

3. Heater | Relatively dependent | Full | Relatively average | much | Relatively average | Relatively average | Relatively average | average | Relatively much | Relatively average | much | little | little | average | average | Relatively low | Relatively low |

4. Combined Cycle | Relatively dependent | Full | average | Very much | little | average | average | little | Relatively average | Relatively average | little | Relatively average | little | little | Relatively average | Relatively low | Relatively low |

5. Nuclear fission | Muchdependent | Relatively complete | Very much | Relatively low | Very very much | Very very much | much | Zero-always on orbit | much | Relatively low | zero | Relatively average | Very very much | zero | Relatively low | Relatively low | Relatively low |

6. Nuclear Fusion | Much dependent | Under international research | Very very much | Very much | average | Extremely high | Very very much | Zero-always on orbit | Very much | Relatively average | zero | Relatively average | Very very much | zero | Relatively average | Relatively low | Relatively low |

7. Diesel | Independent | full | little | Very much | little | little | little | little | Relatively little | little | much | Very little | Very little | average | little | little | Very little |

8. Incineration | Relatively dependent | full | little | Very very much | Very very little | little | Relatively average | Relatively little | average | Relatively average | Relatively little | Very much | Very little | little | Relatively average | Very little | Very little |

9. biomass | Relatively dependent | full | little | Very very much | little | little | Relatively average | Relatively little | average | Relatively average | Relatively little | little | Very little | little | Relatively average | Little | Very little |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

10. Watery | Very much dependent | full | average | Without fuel | zero | Very much | much | little | Very very much | Little | Little (Ecosystem Change) | much | little | Relatively much | much | much | Very very much |

11. Solar thermal | Very dependent | full | little | Without fuel | zero | Relatively much | little | much | Very very much | little | Very very little | Very much | little | zero | Relatively much | average | very |

12. Photovoltaic | Very dependent | full | average | Without fuel | zero | Very much | little | little | Very very | Very very little | Very very little | Very much | Relatively low | zero | Very much | Relatively average | very |

13. windy | Very much dependent | full | average | Without fuel | zero | average | average | little | Very very much | little | Little (influence the birds migration) | Very much | little | little | much | much | Very very much |

14. Fuel cell | dependent | full | average | Very much | average | much | average | little | Very very much | Relatively little | Very very little (silent) | much | Average (Military use) | zero | Relatively average | little | Relatively little |

15. Geothermal5 | Very much dependent | full | average | Without fuel | zero | average | average | little | much | little | little | much | little | Relatively much | Relatively average | average | Very very much |

16. Sea waves | Very much dependent | Relatively full | average | Without fuel | zero | average | Relatively little | little | Very very much | little | Little (influence aquatic life) | average | little | little | much | Relatively little | Relatively little |

17. tidal | Very much | relatively | average | without | zero | much | average | Relatively little | much | little | little | average | little | average | much | average | relatively |

1) Modeling of an issue as a hierarchical system that has the specific objective.

2) Prioritized on the basis of some of the judgments of the ranking is done by comparing two binary.

3) This judgment is combined with a set of priorities for ranking achieved.

4) The harmony of judgments is evaluate.

5) According to the process done final result was obtained (

So the structure and algorithm like what is shown in picture number one is obtained [

After identifying the criteria, different types of power plants are rated against a specific criterion. In this context, a table like

Which E is equal to the estimated annual production based on MWh, H = Heat rate at rated output based on Btu\kWh, F = fuel expected cost based on Mbtu. Which can be said about E: E = Coefficient of utilization\MW nominal capacity of the plant × 8760 hour\year.

It should be said about the estimated annual, it is expected that ideally power plant should be worn at all times with full capacity utilization. In practice it is not possible, because power plant is shut down periodically for maintenance, also, errors inevitably arise which will lead to unscheduled exit, so In relation to E ,thus exploiting coefficient which is a number less than 100% should be used [

Power plant type 1 | Power plant type 2 | Power plant type 3 | …….. | |
---|---|---|---|---|

Power plant type 1 | 1 | a | b | c |

Power plant type 2 | 1\a | 1 | d | e |

Power plant type 3 | 1\b | 1\d | 1 | f |

… | 1\c | 1\e | 1\f | 1 |

Criterion 1.

Description | Definition | |
---|---|---|

Two elements have the same importance | Equally important | 1 |

An element is an average advantage over the other element | Average excellence | 3 |

An element is a huge advantage over the other element | More top | 5 |

An element is very high advantage over the other element | A lot | 7 |

An element is extremely high advantage over the other element | Giving extremely high | 9 |

Borderline cases in judgments | Intermediate values | 2, 4, 6, 8 |

When the element I is compared with j one of the above numbers will be assigned to it. also in comparison I with j reverse number will be assigned (xji = \1 xij) and also all diagonal elements of this matrix is the number one.

Criterion 1 | Criterion 2 | Criterion 3 | ……… | |
---|---|---|---|---|

Criterion1 | 1 | a | b | c |

Criterion 2 | 1\a | 1 | d | e |

Criterion3 | 1\b | 1\d | 1 | f |

………. | 1\c | 1\e | 1\f | 1 |

Technical and non-technical criteria or other criteria.

be for all tables the number in each column should be divided to the total of that column and then calculate the arithmetic mean of each row of the table.

With this work for any of a variety of plants, to the specific criteria, one number is get. This number is the number obtained by performing the same operation on the table. Values are multiplied. Then, for each type of power plant to have a number of different criteria, that the total numbers indicate the final weight of power plants, and this final weight will be the base for the final decision making about selected power plant to construction and operation, However, after evaluating the inconsistency rate that during the following 5 steps will be calculated.

Step 1. Obtain the total weight (WSV): The relative weights of the vector obtained by multiplying a matrix of paired comparisons: WSV = D × W.

Step 2. Obtain the compatibility (CV): Divided by the weighted sum of the vector components of the vector of relative weights are obtained.

Step3. Calculation of the largest matrix of paired comparisons (max): The adjustment is calculated from the mean vector elements.

Step 4: calculation of inconsistency Index (II): Is given by the formula:

Step 5: calculation of inconsistency price (IR): Is given by the formula:

Here IRI (Random inconsistency index) is the value that is get from

If the inconsistency rate is less than or equal with 0.10 (IR 0.10); in paired comparisons there are consistent you can continue to work or decision-maker should review the paired comparisons.

Among other points that should be considered very, is the managers overall strategy. As an example If the overall goal is to minimize the annual and periodic costs of power plants instead initial investment has no restriction, can have a dramatic effect on the rate and also in contrast to the case and the factors influencing the selection process and decisions of senior managers to type of power plants it is true [

Making decision for choosing the best option of power plant, for construction and exploitation, is one of the most important steps in efficiency of and reform of consumption pattern of primary sources in the production, that is also very important and plays a vital role in achieving national smart grid and in order to reach maximum functionality and realize existing potentials (beginning from extraction of primary resources to the targeted use and productivity). As it is seen in the present paper, lots of points and criteria should be considered for choosing power plant, that often or sometimes they cannot be gathered together. In this regard, a clear framework for selecting the type of power plant is very important and useful. It should be noted that the mentioned factors and criteria in this article can be extended to other criteria in this way there is no limit. The mathematical calculation method which is presented in this paper is based on an average and it can be generalized to other methods that each article has its own demands such as: eigenvectors, least squares, logarithmic least squares, rows or weighted least squares geometric mean. In addition, it should be noted that expert group and statistics that weight the tables and the final result is obtained through comparison of their weighting, they are one of the most important parts of the job and better results will be obtain if experienced experts are selected and brainstorm and think tanks are formed. Finally, the last point is determining the overall strategy of managers and announcing it to the experts who rate is so important and it would be better that the managers themselves are also specialists and experts and have the overall view of SWOT (strength, weaknesses, opportunities and threats) analysis of problem.

n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

IRI | 0 | 0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 | 1.51 |

This research was a part of the Ph.D. Research of the first author, conducted in senior research expert in elec- trical energy engineering, Power Technology Development Center. The authors would like to thank all participants in this research, particularly key informants and managers who participated in the research.

The authors declare that they have no competing interests that may be perceived to influence the results and discussion reported in this manuscript.

ShayanHosseini,GevorkGharehpetian,FereshtehFarzianpour, (2015) Fore Sighting and Estimating the Risk of Investing in the Construction of Power Plants Using AHP. Journal of Service Science and Management,08,526-535. doi: 10.4236/jssm.2015.84053