A Review On Optimization Strategies Integrating Renewable Energy Sources Focusing Uncertainty Factor-Paving Path To Eco-Friendly Smart Cities

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS(2021)

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摘要
Renewable and sustainable energy with advancement in information and communication technologies bear huge expectations in power sector. Whereas, switching from traditional to networked power grid requires a long process. Currently, renewable energy (RE) injection into existing power systems is in transition state, which is a sophisticated and multidisciplinary task. In this article, RE integration engineering efforts are discussed that take a step ahead towards green energy. RE integration engineering refers to the controlling and configuring tasks regarding distributed power generation sources, information and communication technologies and dispatchable loads. An extensive literature review of this domain is conducted considering main objectives with associated constraints, techniques used and miscellaneous parameters that lead towards green energy. As with the induction of renewable energy sources (RESs) and utilization of microgrids (MGs), the power generation uncertainty factor is evolved which limits networked grid to operate within its full capacity and perceived advantages. In this study, an hierarchal conceptual framework is also presented that is hybrid in nature, i.e., adds functionalities of both centralized as well as distributed control to avoid bottle necks and complexities to minimize the power generation uncertainty effects. Furthermore, a brief discussion is provided keeping a broader perspective in forth coming power networks for eco-friendly smart cities. The focus of the discussion is on RE integration engineering in perspectives of precise forecasting problem, dynamic dispatch problem, demand responsiveness and market implications that tends to lead towards eco-friendly and power aware smart cities.
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关键词
Renewable energy integration, OPTIMAL recourse management, Ubiquitous energy management, Smart city, Computational intelligence, Ecological concerns, RE generation uncertainty
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