基于遗传算法的荷载模型在不同荷载条件下对DG进行最优的尺寸和选址

V. B. Jaiswal, A. Gautam, S. Singh, J. P. Pandey, R. Payasi
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引用次数: 0

摘要

大多数州/省现在都有可再生能源组合标准,许多州/省要求在未来5到15年内,可再生能源产生的电力销售超过20%。公共政策、激励措施和经济因素共同推动了分布式发电在电力系统中的快速发展。这些需求中的大部分将通过在大型电力系统中增加大量的风能和越来越多的太阳能来解决。由于其“燃料”来源的性质,风能和太阳能发电厂表现出更大的可变性和不确定性。优化是可以用来解决围绕这种可变性和不确定性的问题和成本的工具之一。本报告讨论了运营和市场系统的影响,提供了分布式发电功率输出预测的现实预期的背景,并提出了将预测系统部署到运营使用中的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal sizing & siting of DG with load models using genetic algorithm under different loading conditions
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.
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