Energy Policy
A. Aryanfar; A. Gholami; M. Pourgholi; M. Zandi Zandi; A. Khosravi
Abstract
The current work proposed a novel fuzzy-based multi-criteria decision-making method to assess the development potential of wind power plants in a country. Type-2 fuzzy logic was utilized to investigate the simultaneous effects of several technical criteria such as wind conditions, ambient temperature, ...
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The current work proposed a novel fuzzy-based multi-criteria decision-making method to assess the development potential of wind power plants in a country. Type-2 fuzzy logic was utilized to investigate the simultaneous effects of several technical criteria such as wind conditions, ambient temperature, and dust activities in a site. Iran was chosen as the case study, considering the various environmental conditions and the lack of thorough investigations in the country. The proposed method could be easily extended to apply to any region. The related technical data for all the 559 Synoptic meteorological stations in the country were collected and used as the inputs for the proposed method. Applying two-step interviews with local experts and reviewing the literature, the leading indicators and their effectiveness were defined. After developing the fuzzy rules and sets, all the sites were scored and ranked using type-2 fuzzy logic in the proposed method. Based on the final standings, priority tables were provided and the top fifty sites for implementing offshore and onshore wind power plants were introduced. Moreover, primary analysis of the collected data indicated that the provinces with high energy consumption and high PM 2.5 levels are in critical environmental conditions. Thus, these provinces need strict attention and planning for sustainable energy supply using renewable energy systems. Based on the results, several recommendations and suggestions were also mentioned to organize investment resources for a more efficient and proper power plant development as well as future studies.
M. Mohammadpour; Seyed M. M. Modarres-Gheisari; P. Safarpour; R. Gavagsaz-Ghoachani; M. Zandi
Abstract
Large amplitude inter-well oscillations in bi-stable energy harvesters made them a proper energy harvesting choice due to high energy generation. However, the co-existence of the chaotic attractor in these harvesters could essentially decrease their efficiency. In this paper, an algorithm for detecting ...
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Large amplitude inter-well oscillations in bi-stable energy harvesters made them a proper energy harvesting choice due to high energy generation. However, the co-existence of the chaotic attractor in these harvesters could essentially decrease their efficiency. In this paper, an algorithm for detecting chaos in bi-stable energy harvesters based on a data-gathering algorithm and estimating the largest Lyapunov exponent is investigated. First, a simple model of axially loaded non-linear energy harvesters is derived. This model is derived using the Euler-Bernoulli beam theory and the Assumed Mode method considering the Von-Karman non-linear strain-displacement equation. The harvester's numerical simulation results are used to test the algorithm's efficiency and accuracy in identifying chaotic response. The results showed the algorithm's success in detecting chaos in such systems with minimum possible calculation cost. The effect of noise on the algorithm's performance has been investigated, and the results showed the excellent robustness of the algorithm to noise. It can diagnose the harvester's chaotic or harmonic behavior with noise-contaminated data, with 10 percent noise density. The comparison between this algorithm and Wolf's method showed relatively less computation time, up to 80 percent, to detect chaos with reasonable accuracy.