再生可能エネルギーエンジニアリングの最適化問題 Optimization Problems in Renewable Energy Engineering
- 海川 杨
- 2024年1月18日
- 讀畢需時 2 分鐘
已更新:5月20日
上の図は風力発電所のレイアウトの上からのビューを示す.図(a)の良くない配置から図(b)の良い配置への最適化.図の四角は風力発電機を置ける場所で,青い四角は実際に風力発電機が置いてある位置.赤いシャドウは乱流の影響エリア.
近年,東日本大震災やロシア・ウクライナ紛争によって日本のエネルギー安全保障に影響が出て,エネルギー自給率の向上がより一層求められるようになった.製造業が回帰する一方でエネルギーが不足する日本は.再生可能エネルギーの効率的な利用を研究の焦点とする.例として風力発電所のレイアウト最適化問題(WFLOP)は.与えられる風力発電所と風の分布プロファイルのための電力生産を最大化するために.各風力発電機の最適な位置を決定することを目指す.
The figure above shows a top-down view of a wind farm layout, illustrating the optimization process from a suboptimal configuration (a) to an improved one (b). Squares represent potential turbine placement sites, with blue squares indicating installed turbines. Red shaded areas denote regions affected by wake turbulence.
In recent years, events such as the Great East Japan Earthquake and the Russia-Ukraine conflict have heightened concerns over Japan's energy security, increasing the urgency to improve energy self-sufficiency. As manufacturing returns to Japan amid ongoing energy shortages, the efficient use of renewable energy has become a key focus of research. One prominent example is the Wind Farm Layout Optimization Problem (WFLOP), which aims to determine the optimal placement of wind turbines to maximize energy production based on a given wind farm site and wind distribution profile.
Reference:
Yang, H., Gao, S., Lei, Z., Li, J., Yu, Y., & Wang, Y. (2023). An improved spherical evolution with enhanced exploration capabilities to address wind farm layout optimization problem. Engineering Applications of Artificial Intelligence, 123, 106198. https://doi.org/10.1016/j.engappai.2023.106198
Yang, H., Yu, Y., Cheng, J., Lei, Z., Cai, Z., Zhang, Z., & Gao, S. (2022). An intelligent metaphor-free spatial information sampling algorithm for balancing exploitation and exploration. Knowledge-Based Systems, 250, 109081. https://doi.org/10.1016/j.knosys.2022.109081
Z. Lei, S. Gao, Z. Zhang, H. Yang and H. Li, "A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization," in IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 5, pp. 1168-1180, May 2023, doi: 10.1109/JAS.2023.123387.
Ye, Z., Yang, H., Chiba, N., & Hashimoto, K. (2023, November). Comparative Study of Hybridization and Parameter Tuning Improvement Methods for EAs in WFLOP. In Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems (pp. 59-65). https://doi.org/10.1145/3638209.3638219
Zhang, Z., Yu, Q., Yang, H., Li, J., Cheng, J., & Gao, S. (2024). Triple-layered chaotic differential evolution algorithm for layout optimization of offshore wave energy converters. Expert Systems with Applications, 239, 122439. https://doi.org/10.1016/j.eswa.2023.122439
Yang, Y., Tao, S., Li, H., Yang, H., & Tang, Z. (2024). A Multi-Local Search-Based SHADE for Wind Farm Layout Optimization. Electronics, 13(16), 3196. https://doi.org/10.3390/electronics13163196
Tao, S., Liu, S., Zhao, R., Yang, Y., Todo, H., & Yang, H. (2025). A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization. Mathematics, 13(2), 282. https://doi.org/10.3390/math13020282