Global Electrical Sustainability

Transcription

1 Quest: A Journal of Undergraduate Student Research Page 167 Global Electrical Sustainability Paper 12 By Sarah Budney and Alex Shepherd

2 Paper 12 Quest: A Journal of Undergraduate Student Research Page 168 Abstract: This project assessed the electrically sustainability of various countries using fuzzy logic mathematical techniques. To calculate these overall energy efficiency scores, expert opinion was obtained on various factors related to energy sustainability. These factors were renewable energy growth, amount of renewable resources generated, and amount of nonrenewable resources consumed. The amount of renewable resources generated and nonrenewable resources generated were also dependent on a set of sub-factors. Then, though the use of five analysis methods, Analytical Hierarchy Process (AHP) method, Guiasu Method, Yen Method, Dempster Shafer Theory, and the Set Value Statistical Method, an overall energy sustainability score was calculated for each country and the countries were then ranked according to these scores to determine the most and least energy efficient countries in today s world. Key Words: Electrical Sustainability, Fuzzy Math, Renewable Energy Introduction: With the increasing volatility of fossil fuels prices as well as the increasing insight of the many benefits seen from renewable energy, energy sustainability is starting to become of paramount importance in today s world. As countries adapt to the changing times, many countries are turning towards renewable energy to power their energy demands. Some countries seem to be quite sustainable while other countries seem to be slow to change. This project assessed which countries were the most energy sustainable by ranking them using fuzzy logic mathematical techniques. Previous researchers have used fuzzy mathematical techniques in the past estimate energy consumption as well as assess which types of energy are the most sustainable [1, 2]. However, there has not been any fuzzy mathematical research on ranking countries by how energy sustainable they are. Methods: This fuzzy mathematical model combines energy factors of countries with expert opinion from three different energy experts. The expert opinions weight the significance of each of the factors. This way, countries have to excel in the factors that are heavily weighted in order to have a better energy sustainability score. Factors were ranked on a scale of 0-10, 0 meaning there was no contribution toward energy sustainability and 10 being very important toward sustainability. Factors were picked specifically because of their impact on energy sustainability. The three main factors assessed were amount of renewable energy created annually, amount of non-renewable energy used annually, and the renewable energy growth in the past three years. In addition, the

3 Paper 12 Quest: A Journal of Undergraduate Student Research Page 169 amount of non-renewable and renewable energy factors each had sub-factors assessing the impacts of the various types of energy sources such as coal, nuclear, hydro, solar, wind, and other forms of energy. Table 1: Expert Opinions on Three Major Factors Risk Factors Expert Opinion Expert 1 Expert 2 Expert 3 Growth of Renewable Energy (Last 5 Years) Production of Renewable Energy Use of Non-Renewable Energy Table 2: Expert Opinions on Renewable Energy Production Most Efficient of Renewable Resources Expert 1 Expert 2 Expert 3 Hydro Geothermal Solar Wind Biomass Table 3: Expert Opinions on Non-Renewable Energy Use Use of Non-Renewable Energy Expert 1 Expert 2 Expert 3 Nuclear Coal Natural Gas To create the ranking algorithm, five different fuzzy mathematical methods were used. These methods were the Analytical Hierarchy Method (AHP), Guiasu (G Method), Yen Method, Dempster Shafer Theory, and the Set Value Statistical Method (SVSM). These methods created 5 different equations. Once these equations were created, then energy data for each country could be inputted in these equations to obtain an overall electrical sustainability score. Energy data was obtained from the United States Energy Information Administration website (9). All data was converted to the same units of energy and then normalized according to the equation: (Xmin)/(Max-Min) where x is the participant s score for that factor minus the minimum score from all the participants for that factor divided by the difference between the maximum and minimum score for that factor. This was done to ensure that various units and orders of magnitude did not

4 Paper 12 Quest: A Journal of Undergraduate Student Research Page 170 skew the expert s weight for each factor. Additionally, to account for the fact that the use of nonrenewable energy should negatively contribute to a country s sustainability score, we used the compliment of the data for these sub-factors. This meant that as the amount of non-renewable energy increased, the lower the overall energy efficiency score would be for that country. Next, energy growth was calculated by taking the difference in energy between the last three years and dividing it by the last year output. Finally, all energy data was divided by the countries total energy output to obtain an energy percent. This was done to ensure that the countries size and totally energy production did not create a significant advantage on a countries energy sustainability. Once all the data was incorporated into the algorithms, an energy sustainability score could be calculated and then the countries could then be ranked according to this score on how energy sustainable they were. The different methods analyze the expert opinions in different ways. Thus, the energy sustainability equation changes for each method, but the same data can be used for all methods. The Analytical Hierarchy Method is the most basic of all of the methods we used for analysis. In order to derive the equation for the energy efficiency score, we first find the average of the expert opinions for each factor (find the row averages of the tables). After summing the averages, divide each average by the average total. The resulting number serves as the coefficient for each factor and add to 1 if summed. This means the top score a country can achieve is 1. This is also the case for all of the other methods [13]. Ex: Energy Sustainability Score = 0.25F1+0.5F2+0.25F3 The next method we used was the Guiasu Method. To find the equation for the Guiasu method, we first find the column total of each expert/factor table. Then we divide each element of the column by the corresponding sum. After we have the new values, we find the row average of each factor. These values now serve as the coefficients for the energy sustainability score equation (13). This method helps to speak to the consistency between experts. For example, if one expert ranked all their factors a 10 and another expert ranked all of their factors as a 5, after dividing each factor by its corresponding column total, the 10 and 5 will have the same values. The third method, the Yen method, begins by normalizing all of the expert opinions. Each factor value is divided by the largest factor value that appears in that column. This puts the expert opinions on a scale of 0-1 and 1 is always present. After this normalization, we find the row average. Then we divide each average by the sum of the averages (13). The Dempster Shafer method is somewhat of an extension of the Guiasu method. We begin with the values of the Guiasu table (the values after dividing each element in a given column by the corresponding row column). Then we calculate the row product for each factor. Each row product is divided by the row product total and the resulting number serves as the factor coefficient in the energy sustainability score equation. The procedure in the DSM is based on the following theorem. Let X be a finite set. Let m1, mn be basic probability assignments on X. Suppose m. ([{x})>0 for all x X and i=1, n. then x X m2...n ({x}) = m ({x}) m n ({x}) X X m 1 ({x}) m n ({x}) The final method we used is the Set Value Statistical Method (SVSM). This method uses a ranking process in order to determine the factor coefficients. The lowest weighted factor

5 Paper 12 Quest: A Journal of Undergraduate Student Research Page 171 receives a score of 1 on up to the to the highest ranked factor n, which receives a score of n. If two factors are ranked the same, then the average of that ranking and the next is found, and the resulting value is assigned to both factors. For example, if we have three factors where the first factor has a score of 0.8 and the other two have a score of 0.6, then the first factor will receive a score of 3, and the second two factors will receive a score of 1.5. After finding the different ranking of the factors, we found find the row averages and then sum the averages. The coefficients of the SVSM equation is the row average divided by the row average column. For a further discussion of the SVSM see [12 p. 112]. The Equations and Subsequent Factors: AHP G+ equation G+ = F F F3 Guiasu G+ equation G+ = F F F3 Yen G+ equation G+ = F F F3 DS G+ equation G+ = F F F3 SVSM G+ equation G+ = F F F3 F1 - F1: Growth-of-Renewable-Energy-(Last-5-Years) F2 - F2: Production-of-Renewable-Energy F3 - F3: Use-of-Non-Renewable-Energy ************************************** F2: Production-of-Renewable-Energy Mu table: E1 E2 E3 RowAvg F F F F F Col Sum

6 Paper 12 Quest: A Journal of Undergraduate Student Research Page 172 AHP G+ equation G+ = F F F F F5 Guiasu G+ equation G+ = F F F F F5 Yen G+ equation G+ = F F F F F5 DS G+ equation G+ = F F F F F5 SVSM G+ equation G+ = F F F F F5 F1 - F21: Hydro F2 - F22: Geothermal F3 - F23: Solar F4 - F24: Wind F5 - F25: Biomass ************************************** F3: Use-of-Non-Renewable-Energy Mu table: E1 E2 E3 RowAvg F F F Col Sum AHP G+ equation G+ = F F F3 Guiasu G+ equation G+ = F F F3 Yen G+ equation G+ = F F F3

7 Paper 12 Quest: A Journal of Undergraduate Student Research Page 173 DS G+ equation G+ = F F F3 SVSM G+ equation G+ = F F F3 F1 - F31: Nuclear F2 - F32: Coal F3 - F33: Natural-Gas ************************************** Final Equations AHP final F+ equation: AHPF+ = F F F F F F F F F Guiasu final F+ equation: Guiasu F+ = F F F F F F F F F Yen final F+ equation: Yen F+ = F F F F F F F F F DS final F+ equation: DS F+ = F F F F F F F F F SVSM final F+ equation: SVSM F+ = F F F F F F F F F Factors descriptions in the final equation.

8 Paper 12 Quest: A Journal of Undergraduate Student Research Page 174 F1 - F1: Growth-of-Renewable-Energy-(Last-5-Years) F2 - F21: Hydro F3 - F22: Geothermal F4 - F23: Solar F5 - F24: Wind F6 - F25: Biomass F7 - F31: Nuclear F8 - F32: Coal F9 - F33: Natural-Gas The Results: Country AHP ESS Guiasu ESS Yen ESS DS ESS SVSM ESS ==================== = ========= = ============= = Afghanistan Albania Algeria Angola Argentina Armenia Australia Austria Azerbaijan Bahrain Bangladesh Barbados Belarus Belgium Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Bulgaria Burma (Myanmar) Cambodia Cameroon Canada Chile China Colombia Congo (Brazzaville)

9 Paper 12 Quest: A Journal of Undergraduate Student Research Page 175 Congo (Kinshasa) Costa Rica Cote div.(ivorycoast) Croatia Cuba Cyprus Czech Republic Denmark Dominican Republic Ecuador Egypt Equatorial Guinea Estonia Eurasia Finland France Gabon Georgia Germany Ghana Greece Guatemala Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea, North Korea, South Kuwait Kyrgyzstan Laos

10 Paper 12 Quest: A Journal of Undergraduate Student Research Page 176 Latvia Lebanon Libya Lithuania Luxembourg Macedonia Madagascar Malawi Malaysia Mauritius Mexico Moldova Mongolia Montenegro Morocco Mozambique Namibia Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania Russia Saudi Arabia Senegal Serbia Singapore Slovakia

11 Paper 12 Quest: A Journal of Undergraduate Student Research Page 177 Slovenia South Africa Spain Sri Lanka Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand Trinidad and Tobago Tunisia Turkey Turkmenistan Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Venezuela Vietnam Yemen Zambia Zimbabwe Country AHP Rank G Rank Yen Rank DS Rank SVSM Rank Cyprus Iceland Costa Rica Nicaragua Kenya Norway Guatemala Jamaica Uruguay

12 Paper 12 Quest: A Journal of Undergraduate Student Research Page 178 Brazil Sweden Sri Lanka Switzerland New Zealand Mauritius Austria Honduras Panama Papua New Guinea Chile Canada Portugal Finland Denmark Bhutan Spain China Philippines Estonia New Caledonia Slovenia Senegal India Zambia Germany Greece Namibia Malawi Paraguay Swaziland Montenegro Lebanon Nepal Mongolia Korea, North Zimbabwe Botswana Niger Laos Madagascar Albania Turkey

13 Paper 12 Quest: A Journal of Undergraduate Student Research Page 179 Macedonia Bosnia and Herzegovina Congo (Kinshasa) South Africa Ireland Italy Poland Serbia Morocco Czech Republic Mozambique Bulgaria Kazakhstan Australia Japan Israel United States Hong Kong Slovakia France Taiwan Mexico Afghanistan Indonesia Kyrgyzstan Romania United Kingdom Iraq Vietnam Cuba Russia Belgium Korea, South Netherlands Hungary Luxembourg Dominican Republic Puerto Rico Thailand Nigeria Malaysia Croatia Latvia

14 Paper 12 Quest: A Journal of Undergraduate Student Research Page 180 Tajikistan Ukraine Georgia Colombia Eurasia Singapore Burma (Myanmar) Peru Argentina Bolivia Tunisia Angola Azerbaijan Pakistan Bangladesh Armenia Cote div.(ivorycoast) Egypt Uzbekistan Jordan Belarus Moldova Iran Algeria United Arab Emirates Trinidad and Tobago Bahrain Turkmenistan Barbados Kuwait Oman Qatar Saudi Arabia Yemen Libya Brunei Equatorial Guinea Tanzania Lithuania Syria Gabon Venezuela Congo (Brazzaville)

15 Paper 12 Quest: A Journal of Undergraduate Student Research Page 181 Ecuador Ghana Cameroon Cambodia ===================== =========== ======= Discussion: The fuzzy mathematical energy sustainability scores do not have a direct interpretation. Therefore, we do not exactly know what a score of for Canada means. However, the scores do allow us to compare the sustainability between various countries and give an overall ranking by country for how energy efficient a country is. These rankings show some very interesting results. Cyprus ranked number one as the most energy sustainable country. Although Cyprus may not be the first country many think about when they think of the top countries for energy sustainability, Cyprus may be more efficient than suspected. Recently, Cyprus won the World Renewable Energy Congress Trophy due to its recent achievements in their increased growth for renewable energy sources. Part of this recent growth may be due to its high energy fossil fuel transportation costs. Since Cyprus is an island, it has isolated from many other parts of the world, and thus makes it more difficult to transport fossil fuels to the island, resulting in higher energy transportation costs. This could, in turn, shift energy demand away from fossil fuels in Cyprus, thereby avoiding high transportation costs, and thus helping to shift demand toward more renewable energy sources. Also, the fact that we controlled for country size could have also helped to push Cyprus up to the top of the list for the most energy sustainable country. Lastly, their recent investment in solar power may also have greatly helped increase their energy sustainability score [3, 4]. It is not surprising that Iceland came in number two as the second most energy sustainable country. With its huge supply of geothermal energy production along with its impressive hydroelectric power, Iceland is definitely a very sustainable country. Also it is not surprising that Costa Rica and Nicaragua came in third and fourth on this list. They have lead the way in their region for energy efficiency and have obtained a good reputation over the last decade as very impressive examples of renewable energy efficiency [5]. Lastly, it is interesting to see that Kenya, an African country ranked in the top 5 countries. However, it is well known that Kenya has lead the way in renewable energy production in Africa and in addition, it has made huge investments recently in photovoltaic solar power production. All of this could help to explain why it was able to score in the top 5 most sustainable countries in the world [6]. The United States seemed to rank in the middle, which makes sense because although they create a lot of renewable energy, they are also the number one country for non-renewable energy use [8]. Looking at the bottom of the list also shows some interesting results. Most of the countries are either Middle Eastern countries like Saudi Arabia or Kuwait or places in Latin America like Venezuela and Ecuador. This seems to make a lot of sense as many of these countries have large reserves of fossil fuels and thus a greater ability to use fossil fuels for energy demand. These large supplies of fossil fuels, in turn, reduce demand for renewable energy growth since cheaper fossil fuel energy will drive demand away from investing in the more expensive alternative, renewable energy production. Therefore, it is no surprise that these countries with large fossil fuel supplies rank near the bottom of the list as they use large amounts of fossil fuels with relatively low renewable energy growth [7, 10].

16 Paper 12 Quest: A Journal of Undergraduate Student Research Page 182 Also looking at the list shows that there are a few African countries that are near the bottom of the list like Ghana and Cameroon. Although these countries do not have a large supply of fossil fuels like other countries near the bottom of the list, these African countries seem to lack renewable energy production and growth. This is probably due to lack of investment capital for these renewable energy projects. However, there is hope that many of these countries can leapfrog forward in energy development by skipping the traditional fossil fuel route and go directly toward more efficient and sustainable energy transitions in future energy development [11]. Future research will be needed to analyze if capital investments and government spending may be directly correlated to energy sustainability. Also, future research could also include more factors related to energy sustainability to gain a broader analysis of a country s energy sustainability. Lastly, obtaining more expert analysis would be beneficial in future studies to gain a better understanding of how each factor affects energy sustainability. Conclusion: This project used fuzzy mathematical models to rank countries according to how sustainable each country is in terms of the amount and growth of renewable energy produced by each country as well as the amount of non-renewable energy each country used. It was seen that Cyprus, Iceland, and Costa Rica may be some of the most energy efficient countries in the world while some countries in the Middle East and Latin America may be some of the least energy efficient due to large supplies of fossil fuels. Future directive will help to improve the algorithm for a greater analysis of energy sustainability. We remain optimistic that this method can be used to improve energy sustainability in the future, but for the moment we provide the first assessment of ranking energy sustainability by country using fuzzy mathematical techniques.

17 Paper 12 Quest: A Journal of Undergraduate Student Research Page 183 References: 1. Jabera, J.O., R. Mamlook, and W. Awad. "Evaluation of energy conservation programs in the residential sector using fuzzy logic methodology." Energy Policy, 33: Print. 2. Azadeh, Ali, Morteza Saberi, and Anahita Gitiforouz. "An integrated simulation-based fuzzy regression-time series algorithm for electricity consumption estimation with nonstationary data." Journal of the Chinese Institute of Engineers 34: Print. 3. Kalogirou, Soteris. "Solar water heating in Cyprus: current status of technology and problems." Renewable Energy 10: Print. 4. Michaelides, J.M.. "Exploitation of solar energy in Cyprus." Renewable Energy 1: Print. 5. Van den Akker, Johannes. "Energy Efficiency in El Salvador, Nicaragua, Costa Rica, Panama." United Nations Development Programme 1: Print. 6. Karekezi, Stephen, and Waeni Kithyoma,. "Renewable Energy in Africa: Prospects and Limits." United Nations Sustainable Development. N.p., 2 June Web. 22 Apr "Supply and Demand." The National Acadamies. N.p., 1 Jan Web. 22 Apr < 8. Turner, John. "A Realizable Renewable Energy Future." Advancing Science, Serving Humanity 285 (): Print. 9. "U.S. Energy Information Administration - EIA - Independent Statistics and Analysis."Countries. Energy Information Administration, 1 Mar Web. 30 Apr < 10. Berndt, Ernst, and David Wood. "Technology, Prices, and the Derived Demand for Energy." The Review of Economics and Statistics 57: Print. 11. Murphy, James. "Making the energy transition in rural east Africa: Is leapfrogging an alternative?." Technological Forecasting and Social Change 68: Science Direct. Web. 29 Apr Hong Xing, Li and Vincent C. Yen, Fuzzy Sets and Fuzzy Decision Making, CRC Press, Mordeson, J.N., M.J. Wierman, T.D. Clark, A. Pham, and M.A. Redmond, Linear Models In Mathematics of Uncertainty, Studies in Computational Intelligence 463, Springer 2013.

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