Optimal Sizing & Siting of DG with Load Models using Genetic Algorithm under Different Loading Conditions

被引:0
作者
Jaiswal, V. B. [1 ]
Gautam, A. K. [1 ]
Singh, S. P. [1 ]
Pandey, J. P. [1 ]
Payasi, R. P. [1 ]
机构
[1] Kamla Nehru Inst Technol, EED, Sultanpur 228118, UP, India
来源
2016 INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ELECTRICAL ELECTRONICS & SUSTAINABLE ENERGY SYSTEMS (ICETEESES) | 2016年
关键词
Distributed System; Distributed Generation; Load Model; Genetic Algorithm; Optimal Sizing & Siting; DISTRIBUTED GENERATION; DEFINITION; BENEFITS;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The majority of states/provinces now have renewable portfolio standards, with many requiring that over 20 percent of electricity sales be generated by renewable energy sources within the next five to fifteen years. A combination of public policy, incentives and economics is driving a rapid growth of distributed generation in the electric power system. The majority of these requirements will be addressed by adding significant amounts of wind energy and growing amounts of solar energy to the bulk power system. Wind and solar power plants exhibit greater variability and uncertainty because of the nature of their "fuel" sources. Optimization is one of the tools that can be used to address concerns and costs around this variability and uncertainty. This report discusses operational and market system impacts, provides background on what can be realistically expected from distributed generation power-output forecasting, and proposes recommendations to deploy forecasting systems into operational use.
引用
收藏
页码:139 / 146
页数:8
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