Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission
Power system planning is a capital intensive investment-decision problem. The majority of the conven- tional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energ...
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| Veröffentlicht in: | Frontiers in Energy 2015-03, Vol.9 (1), p.91-105 |
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| creator | Kumar, Deepak Mohanta, D. K. Reddy, M. Jaya Bharata |
| description | Power system planning is a capital intensive investment-decision problem. The majority of the conven- tional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO2 emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design prac- tices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies. |
| doi_str_mv | 10.1007/s11708-015-0345-y |
| format | Article |
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An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design prac- tices. 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K.</creatorcontrib><creatorcontrib>Reddy, M. Jaya Bharata</creatorcontrib><title>Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission</title><title>Frontiers in Energy</title><addtitle>Front. Energy</addtitle><addtitle>Frontiers in Energy</addtitle><description>Power system planning is a capital intensive investment-decision problem. The majority of the conven- tional planning conducted since the last half a century has been based on the least cost approach, keeping in view the optimization of cost and reliability of power supply. Recently, renewable energy sources have found a niche in power system planning owing to concerns arising from fast depletion of fossil fuels, fuel price volatility as well as global climatic changes. Thus, power system planning is under-going a paradigm shift to incorporate such recent technologies. This paper assesses the impact of renewable sources using the portfolio theory to incorporate the effects of fuel price volatility as well as CO2 emissions. An optimization framework using a robust multi-objective evolutionary algorithm, namely NSGA-II, is developed to obtain Pareto optimal solutions. The performance of the proposed approach is assessed and illustrated using the Indian power system considering real-time design prac- tices. The case study for Indian power system validates the efficacy of the proposed methodology as developing countries are also increasing the investment in green energy to increase awareness about clean energy technologies.</description><subject>Alternative energy sources</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>Clean energy</subject><subject>Clean technology</subject><subject>Climate change</subject><subject>Developing countries</subject><subject>Electric utilities</subject><subject>Electricity generation</subject><subject>Emission standards</subject><subject>Emissions</subject><subject>Energy</subject><subject>Energy consumption</subject><subject>Energy industry</subject><subject>Energy resources</subject><subject>Energy Systems</subject><subject>Expansion</subject><subject>Fossil fuels</subject><subject>Global climate</subject><subject>Green energy</subject><subject>Infrastructure</subject><subject>LDCs</subject><subject>Optimization</subject><subject>Optimization techniques</subject><subject>Pareto optimum</subject><subject>Pareto最优解</subject><subject>Planning</subject><subject>Portfolio performance</subject><subject>Renewable energy sources</subject><subject>Renewable resources</subject><subject>Research Article</subject><subject>Studies</subject><subject>Technology</subject><subject>Volatility</subject><subject>二氧化碳排放量</subject><subject>优化集成</subject><subject>可再生资源</subject><subject>投资组合理论</subject><subject>混合燃料</subject><subject>燃油费</subject><subject>电力系统规划</subject><issn>2095-1701</issn><issn>2095-1698</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kE9LxDAUxIsoKOoH8Bb0avW9pk3boyz-A8GLnkPavqzRbrImWXX99GapiidPGcj8ZniTZUcIZwhQnwfEGpocsMqBl1W-3sr2CmirHEXbbP_oGnA3OwzhGQAQoYK62Mvebm2kcTRzspG5ZTQL86micZY5zTxZelfdSEkFt_I9sYX5oMCM7Z1fOp-cds7iEzHSmvq4gfSKRuZNeDmdZO9CZMoObHZfMFqYEFL6Qbaj1Rjo8Pvdzx6vLh9mN_nd_fXt7OIu73nJY67buhOK6loVnR6ahrAcsBFQak5KUaVUV4AeUOiGV92gupZj-oOBdKla1Hw_O5lyl969rihE-ZzusKlSoqhEIRAbnlw4uXrvQvCk5dKbhfJriSA3C8tpYZkWlpuF5ToxxcSE5LVz8n-S_4GOv4uenJ2_Ju63SYiyKCsuSv4FFNiMeQ</recordid><startdate>20150301</startdate><enddate>20150301</enddate><creator>Kumar, Deepak</creator><creator>Mohanta, D. 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| subjects | Alternative energy sources Carbon dioxide Carbon dioxide emissions Clean energy Clean technology Climate change Developing countries Electric utilities Electricity generation Emission standards Emissions Energy Energy consumption Energy industry Energy resources Energy Systems Expansion Fossil fuels Global climate Green energy Infrastructure LDCs Optimization Optimization techniques Pareto optimum Pareto最优解 Planning Portfolio performance Renewable energy sources Renewable resources Research Article Studies Technology Volatility 二氧化碳排放量 优化集成 可再生资源 投资组合理论 混合燃料 燃油费 电力系统规划 |
| title | Intelligent optimization of renewable resource mixes incorporating the effect of fuel risk, fuel cost and CO2 emission |
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