183. Bhat PR, Chanakya HN, Ravindranath NH. Biogas plant dissemination: success story of Sirsi, India. Energy Sustainable Dev 2001. 39-41 (March (1).
184. MNRE. Biomass power and cogeneration programme of the Ministry of New and Renewable Energy. Government of India 2013.
185. Global status report REN 21, (http://www.cenrec.com.au/wpcontent/uploads/2014/03/GSR2013_lowres.pdf).
186. Sudha P. Plantation forestry; land availability and bio-mass production potential in Asia. Report submitted to ARPEEC, Sida, Energy Program, AIT, Bangkok; 1996.
187. Annual report of Pabiomass energy association; January, 2013.
188. Annual Energy Outlook 2014. Published December 16, 2013 with the final release of the full AEO 2014 presently slated for April 30, 2014, (http://www.nirs.org/alternatives/sundayforecast414.pdf); 2014.
189. Annual Akshay Urja report MNRE, (http://mnre.gov.in/mission-and-vision-2/publications/akshay-urja/)
*Corresponding author: [email protected]
2
Assessment of Renewable Energy Technologies Based on Sustainability Indicators for Indian Scenario
Anuja Shaktawat1* and Shelly Vadhera2
1SREE, NIT Kurukshetra, Haryana, India
2Department of Electrical Engineering, NIT Kurukshetra, Haryana, India
Abstract
Renewable energy (RE) technologies in India, i.e., large hydropower, small hydropower, onshore wind power, solar photovoltaic (PV), and bioenergy are assessed and ranked based on selected sustainability indicators. Sustainability assessment of RE technologies at a national scale involves a range of conflicting indicators. Multicriteria decision-making (MCDM) methods are the best tool that can address these conflicts. However, the present assessment used a qualitative scale for some indicators. These qualitative data are associated with uncertainties and a fuzzy MCDM approach is the best tool to address these associated uncertainties in data. Thus, the study ranks the RE technologies in context to India under associated uncertainties using fuzzy-TOPSIS, a well-known MCDM method. Further, it has been reviewed that indicator values vary widely for each RE technology. Accounting for these uncertainties in input data the TOPSIS is run using Monte Carlo simulation (MCS) to obtain probabilistic ranking. To understand the impact of these uncertainties both the fuzzy-TOPSIS and probabilistic ranking are compared with that obtained from the TOPSIS method and are found to be uncertain. Thus, the study concludes that for better decision-making and energy planning at the national level the uncertainties in input data must be addressed while assessing the sustainability.
Keywords: Renewable energy, risks, sustainability assessment, sustainability indicators, uncertainties
Nomenclature
| AHP | Analytic hierarchy process |
| CEA | Central electricity authority |
| CFA | Central financial assistance |
| CSP | Concentrated solar power |
| ELECTRE | Elimination and choice translating reality |
| GHG | Greenhouse gas |
| GW | Gigawatt |
| HPO | Hydropower purchase obligation |
| JNNSM | Jawaharlal Nehru national solar mission |
| MCDM | Multicriteria decision making |
| MCS | Monte Carlo simulation |
| MW | Megawatt |
| NIWE | National institute of wind energy |
| PROMETHEE | Preference ranking organization method for enrichment evaluation |
| PV | Solar photovoltaic |
| RE | Renewable energy |
| TOPSIS | Technique for order of preference by similarity to ideal solution |
| WSM | Weighted sum method |
2.1 Introduction
Fossil fuels-based generation contributes the maximum towards electricity generation in most parts of the world [1]. The increased use of energy from fossil fuels is the major cause of the environmental problem which the world is facing today. To improve the environmental conditions along with economic and social development, both developed and developing countries have already initiated the adoption of suitable energy systems to achieve sustainable development. Development is not possible without energy and sustainable development is not possible without sustainable energy. Thus, it becomes important that the sustainable energy system should be efficient and at the same time limit emissions.
Global warming as a result of increased greenhouse gas (GHG) emissions from fossil fuel-based generation has led the world to gradually switch to renewable energy (RE) based generation. RE sources e.g., wind, solar, hydropower, ocean, geothermal, biomass, etc., have been recognized as a key player in reducing GHG emissions and a path towards a sustainable future [2]. Today one of the most important goals towards achieving sustainability is the transformation to an RE-based economy [3]. However, there are various factors that help to attain sustainable development. For example, the energy sources should be readily available in the long term, should be efficient, and should not cause any form of social impacts while they are being utilized [2].
India along with many other countries has already taken many initiatives to develop and promote RE-based generation [4]. The regular increasing energy demand in India is met by both commercial and RE sources. In India, the RE supports the government in meeting the country’s three important energy policy objectives, i.e., energy access, energy security, and climate change mitigation, along with reducing dependency on the importing of fossil fuels [5, 6].
RE sources are inexhaustible, environmentally friendly, have zero fuel cost except for bioenergy, and help in local development by creating employment opportunities. At the same time, RE has high installation costs, intermittent sources (solar and wind), and environmental and social impacts in the form of landscape alteration, deforestation, loss of biodiversity, displacement of people, etc. [7–12]. Sustainability in energy systems refers to economic development along with protecting the environment and society. Sustainability dominants as one of growth, while assessing the energy.