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3Fuel and energy complex of Russia has always played an important role in the econ-omy of the country. During the reform period due to sharp decline in output in all sectors of the economy the role of the complex increased.
At the heart of improving the efficiency of any production the saving productive re-sources of all kinds lay. Due to the continuous rise of energy costs in the country, increase of the gas transportation cost price, nonrenewability of natural resources, the most important areas of work in the field of gas trunk transport should be considered the efforts, aimed at reducing the energy and cost savings, as well as modern approaches are relevant to assess the level of sustainable consumption and energy resources. The object of research is the subsidiary of "Gazprom", engaged in gas transmission.
The enterprise assessment of energy consumption is fragmented and does not fully reflect the effectiveness of their use in general. Therefore, there is the need for such meth-ods, which will assess to evaluate the effectiveness of energy use in the workplace and work power as the whole, in particularly in relation to pipeline transport of gas.
In the previous study [1], the authors considered tenths effective method for estimating energy consumption on the enterprises of gas trunk transport. In this study, the authors propose to apply the analytic hierarchy process (developed by T. Saati) for evaluating the effectiveness of the generalized energy consumption in gas trunkline.
The purpose of the hierarchy method analysis is to identify the directions of irrational energy consumption in the enterprise, using the ranking of these areas on the importance and priority.
In accordance with the proposed methodology, conclusion on the effectiveness of consumption of energy is given on the basis of test results such areas as accounting, control and rationing of energy consumption; energy consumption by the fields of use and ways to improve energy efficiency.
At the first stage the hierarchy was built, which includes six levels: focus, primary factors, actors, goals, actors' goals, contrasting scenarios and generalized scenario (Fig. 1).
Then a set of matrices of pairwise comparisons for each of the lower levels, one for each element of the matrix for each top level, was constructed (Table 1-2). To establish the relative importance of the hierarchy elements the ratio scale was used [2].
On the second level of the hierarchy, there is only one matrix of paired comparisons, which determines the factor with greater influence on the rational use of energy. Calculation is carried out on the principal of eigenvector W, consisted from (W1, W2, W3): W1 = 1,4 / 16,73 = 0,08; W2 = 9 / 16,73 = 0,54; W3 = 6,33 / 16,73 = 4,20.
The calculations show that the most dominant factor is the consumption of energy by the fields of use - 0,54, second place is the work to improve energy efficiency - 0,38.
Each pair of actors is compared to the level of the relative factors impact. The results are in Table 2. Further the importance of the actors' goals are determined. The goals of each 8 actor were compared in pairs. As the result there are the priority actors, showing the order and weight, and thus on the basis of Table 2 decision-making matrix is built in Table 3.
FIG. 1. LEVEL HIERARCHY
TABLE 1
THE DEGREE OF FACTORS INFLUENCE ON THE RATIONAL USE OF ENERGY
Factor
Factor Accounting, control and normalization of resources consumption Energy consumption by the fields of use Work to improve efficiency Total
Accounting, control and normalization of resources con- 1,00 0,20 0,20 1,40
sumption
Energy consumption by the fields of use 5,00 1,00 3,00 9,00
Work to improve efficiency 5,00 0,33 1,00 6,33
Total 11,00 1,53 4,20 16,73
W 0,08 0,54 0,38
Amax 3,34
TABLE 2
THE DEGREE OF ACTORS INFLUENCE ON RATIONAL FACTORS OF ENERGY USE
Actor Fa croc
Accounting, control and normalization of resources consumption Energy consumption by the fields of use Work to improve efficiency
System of commercial energy account 0,35
System of energy account by shops 0,45
System of energy use regulation 0,20
Equipment and complexes 0,50
System of comfort conditions 0,30
Technological operations 0,20
Removing reprimands 0,35
New technologies use 0,25
Financial supply 0,40
Amax 6,38 6,70 5,91
TABLE 3
THE MATRIX OF DECISION-MAKING
A 6 c X
0,35 - - 0,0294
0,45 - - 0,0378
0,20 - - 0,0168
- 0,50 - 0,2690
- 0,30 - 0,1614
- 0,20 - 0,1076
- - 0,35 0,1323
- - 0,25 0,0945
- - 0,40 0,1512
On the next stage, there is the degree of actors' importance, concerning factors for the future sustainable use of energy resources. To determine the influence of factors on the future of the rational use of energy resources in the enterprise the following calculations were made. Each value of A, B, C is multiplied by the corresponding value of W. The result is the sum of each actor. We can conclude which of them has the greatest impact on the primary factors, influencing the rational use of energy resources in the enterprise.
Because of equipment and complexes actors, comfort conditions system and financial supply have for more than 50 % of exposure to the primary factors, influencing the rational use of energy, in the future we will use these actors to get the balance scenario.
Now we find the important goals for the actors, multiplying the eigenvector correspond-ing weight goals for the actor:
Using six goals with maximum value and normalizing their weight, we receive the fol-lowing result vector of weights goals. This requires a normalization factor. Knorm = 1 / I major goals = 1 / 0,4255 = 2,351.
Multiplying the vector of the important actors' goals on the normalization factor, we obtain the result vector of weights goals. The sum of the resulting vector is equal to 1.
0,253
0.1076 0,0942 0,0533 0,0646 0,0302 0,0756
* 2,351 =
0.221 0,126 0,152 0.071 0,177
The resulting normalized vector of priorities will be applied in what follows for the balance scenarios.
On the next step, the degree of impact scenarios on the actors' goals is determined. Results of pairwise comparisons processing matrices are presented in Table 4.
For weights scenarios, relative to the focus of the hierarchy (rational use of energy), we multiply matrix, formed from the values of the vectors of priorities scenarios for the pur-poses of weight vector (Table 5).
Then each value of the priority vector scenarios matrix multiply on the resulting vector of weights goals and obtain:
Gas save = (0,3 x 0,253) + (0,55 x 0,221) + (0,3 x 0,071) + (0,15 x 0,177) = 0,246. Electric energy save = (0,15 x 0,253) + (0,3 x 0,152) + (0,15 x 0,71) + (0,2 x 0,177) = 0,13. Heat save = (0,2 x 0,126) + (0,15 x 0,071) + (0,2 x 0,177) = 0,096.
Reduction of unreasonable energy losses = (0,55 x 0,253) + (0,45 x x 0,221) + (0,2 x 0,126) + (0,2 x 0,152) + (0,4 x 0,071) + (0,45 x 0,177) = 0,4.
Creating comfortable working environment = (0,6 x 0,126) + (0,5 x 0,152) = 0,165. The analysis of the resulting vector of priorities shows that the scenario " Reduction of unreasonable energy losses " has the greatest weight - 0,4, and therefore is the most likely.
TABLE 4
RESULTS OF PROCESSING THE PAIRWISE COMPARISONS MATRIX
Actor's goal
Scenan'o Reliability of the equipment Supplying production process Maintaining the specified parameters of air The required level of illuminance Control over the flow of energy Attract staff to energy issues
Energy saves
1. Gas 0,30 0,55 0,30 0.15
2. Electric energy 0,15 0,30 0,15 0,20
3. Heat energy 0,20 0,15 0,20
Reduction of unrea-
sonable energy 0,55 0,45 0,20 0,20 0,40 0,45
losses
Creating comfortable working environment 0,6 0,50
TABLE 5
THE MATRIX OF VECTORS PRIORITY SCENARIOS
A B C D E F
0,3 0.55 - - 0,30 0,15
0,15 - - 0.30 0,15 0,20
— — 0,20 — 0,15 0,20
0,55 0.45 0,20 0.20 0,40 0.45
- - 0,60 0.50 - -
At the last stage, the effects of the adoption of the most probable scenarios are deter-mined and the generalized scenario is assessed. Knowing the relative weights of scenarios, obtained in the previous step, the generalized scenario can be generated. Generalized measure on the scale for the state variable is determined by summing up the weights of scenarios with the corresponding values of a variable state.
Generalized value for all scenarios = 1,031 + 1,031 + 1,047 + 4,142 + + 3,401 + 3,366 + 2,002 + 3,195 + 2,094 = 21,309. Value on a generalized scale, equal to 21,309, is not "weight" or priority rank, it is used as the global measure or benchmark, against which can be measured appropriately degree of similarity between the probable and desirable future. The results of our analysis with respect to gauge the state variables under consideration scenarios are presented in Table 6.
In conclusion, it can be noted that the application of the analytic hierarchy method all the qualitative and quantitative sources of resource consumption were discussed in details. The analysis of the resulting vector priorities, which showed that the scenario of "unreason-able losses reduction" has the greatest weight - 0,4, and therefore is the most likely. We identified the consequences of making the most likely scenarios, and evaluated the gener-alized scenario. Value on a generalized scale was 21,309 (out of 30). Evaluation of the state variables with respect to gauge considered scenarios allowed us to make the following con-clusions: the situation with gas savings in gas transport system in the nearest future is likely to change for the better one. The greatest influence on changes that have work - energy- consuming equipment and systems present in whole or in types of finite energy resources due to more reliable operation and ensure the smooth production process. Also technologi-cal processes influence on gas savings, which can be achieved by
increasing the efficiency of operation of trunk steel pipelines, ensure the normal course of the process and maintain a set of technical condition of the equipment. Gas losses are possible due to the introduction of new technologies.
1. Electricity and heat energy savings are mainly due to changes in the system of equations comfort. Our study indicated that workers of enterprises of gas transmission sys-tem have comfort conditions and are given special attention. It is important to note that the economy of electric and heat energy affects financial incentives staff. In the gas transmission system there is a system of bonuses for the rational use of energy resources. In the study [3] the scale of penalties for inefficient use of energy resources is suggested. 2. The decrease of unreasonable losses occur due to more efficient use of equipment and facilities, manufacturing operations, as well as by removing comments and improvement of the material stimulate.
The study identified the key areas that they need to pay attention to improve the rational use of energy, namely, equipment and systems; system comfortable conditions; financial incentives.
TABLE 6
DETERMINING THE CONSEQUENCES OF THE ADOPTION OF THE MOST PROBABLE SCENARIOS
Variable of condition (criteria for consequences estimation) Scenario and its weight
Gas save Electric energy save Heat energy save Reduction of unreasonable energy losses Creating comfortable working environment Total weight Optimal variant
Weigh 0,24 6 0,13 0,096 0,4 0,165
Accounting, control and normalization of resources consum ption
System of commercial account +1 +1 +1 +1 +1 + 1,031 +1,5
System of account by shops +1 +1 +1 +1 +1 + 1,031 +1,5
System of consumption normalization +2 +2 +1 +1 +1 + 1.047 +2
Energy consumption by the fields of use
Equipment and complexes +5 +3 +2 +5 +2 +4,142 +5
System of comfort conditions +1 +5 +5 +3 +5 +3,401 +5
Technological operations +4 +2 +2 +4 +2 +3,366 +5
Work to improve efficiency
Removing reprimands +1 +1 +1 +3 +2 +2.002 +3
New technologies use +2 +3 +3 +3 +5 +3,195 +4
Financial supply +3 +1 +1 +2 +2 +2.094 +3
Total value of all scenarios +21,309 +30
REFERENCES
1. Vazhenina LV integrated approach to assessing the efficiency of energy consumption in enterprises gas trunkline / / Herald INZhEKONa. Series "Economy". - You start-1 (24). - St. Petersburg, 2009. - S. 359-362.
2. ANDREICHENKO AV, Andreychenkova O. Analysis, synthesis, planning decisions in the economy. - Moscow: Finance and Statistics, 2000. - 368.
3. Ibid
