Karthik N, Rajagopalan Arul, Bajaj Mohit, Medhi Palash, Kanimozhi R, Blazek Vojtech, Prokop Lukas
Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, Chennai, Tamilnadu, India.
Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, Chennai, Tamilnadu, India.
Sci Rep. 2024 Aug 16;14(1):18997. doi: 10.1038/s41598-024-69734-4.
Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm's efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.
由于可再生能源具有高可靠性、能源独立性、高效性和环境效益，研究人员越来越关注它。本文介绍了一种用于微电网（MG）短期调度的新型多目标框架，该框架解决了最小化运营成本和减少污染排放这两个相互冲突的目标。核心贡献是开发了混沌自适应正弦余弦算法（CSASCA）。该算法同时生成帕累托最优解，有效平衡了成本降低和排放减少。该问题被表述为一个具有成本降低和环境保护目标的复杂多目标优化任务。为了增强算法内的决策制定，引入了模糊逻辑。在三种场景下评估了CSASCA的性能：（1）光伏和风电机组满功率运行；（2）所有机组在指定限制内运行且公用事业电力交换不受限制；（3）微电网仅使用零排放能源运行。第三种场景突出了该算法在先前研究未涉及的具有挑战性的情况下的有效性。使用三个测试示例将这些场景的仿真结果与传统正弦余弦算法（SCA）和其他近期优化方法进行了比较。CSASCA的创新之处在于其混沌自适应机制，这显著提高了优化性能。这些机制的整合为运营成本、排放和执行时间带来了更优的解决方案。具体而言，在第一种场景中，CSASCA实现了成本最优值590.45 €ct和排放最优值337.28 kg；在第二种场景中，成本最优值为98.203 €ct，排放最优值为406.204 kg；在第三种场景中，成本最优值为95.38 €ct，排放最优值为982.173 kg。总体而言，CSASCA通过提供增强的探索能力、改进的收敛性、有效的约束处理和降低的参数敏感性，优于传统的SCA，使其成为解决微电网调度等多目标优化问题的有力工具。
