Shahri Hassan Yaghubi, Hosseini Seyed Ali, Pourhossein Javad
Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran.
Heliyon. 2024 Sep 6;10(18):e37476. doi: 10.1016/j.heliyon.2024.e37476. eCollection 2024 Sep 30.
Reducing thermal unit operating costs and emissions is the goal of the multi-objective issue known as multi-area economic/emission dispatch (MAEED) in smart grids. Using renewable energy (RE) have significantly lowered greenhouse gas emissions and ensured the sustainability of the environment. With regard to constraints such as prohibited operating zones (POZs), valve point effect (VPE), transmission losses in the network, ramp restrictions, tie-line capacity, this study aims to minimize operating costs and emission objectives by solving the multi-area dynamic economic/emission dispatch (MADEED) problem in the presence of RE units and energy storage (ES) systems. The conventional economic dispatch (ED) optimization approach has the following shortcomings: It is only designed to solve the single-objective optimization problem with a cost objective, in addition, it also does not have high calculation accuracy and speed. Therefore, to address this multi-objective MADEED problem with non-linear constraints, this paper introduces hybrid particle swarm optimization (PSO)-whale optimization algorithms (WOA). The reason for combining two algorithms is to use the advantages of both algorithms in solving the desired optimization problem. The introduced method is tested in two separate scenarios on a test network of 10 generators. Using the suggested hybrid methodology in this study, the MADED and MADEED problems are resolved and contrasted with other evolutionary techniques, such as original WOA, and PSO methods. Examining the results of the proposed method shows the efficiency and better performance of the proposed method compared to other methods. Finally, the results obtained by simulations indicate that integrating the necessary system restrictions gives the system legitimacy and produces dependable output. With regard to the results obtained from the introduced approach, the value of the overall cost function has clearly decreased by about 3 % compared to other methods.
降低热力机组运营成本和排放是智能电网中多区域经济/排放调度（MAEED）这一多目标问题的目标。使用可再生能源（RE）已显著降低温室气体排放并确保了环境的可持续性。针对诸如禁止运行区（POZ）、阀点效应（VPE）、网络传输损耗、爬坡限制、联络线容量等约束条件，本研究旨在通过解决存在可再生能源机组和储能（ES）系统情况下的多区域动态经济/排放调度（MADEED）问题，来最小化运营成本和排放目标。传统的经济调度（ED）优化方法存在以下缺点：它仅设计用于解决以成本为目标的单目标优化问题，此外，其计算精度和速度也不高。因此，为解决这个具有非线性约束的多目标MADEED问题，本文引入了混合粒子群优化（PSO）-鲸鱼优化算法（WOA）。结合两种算法的原因是利用它们在解决所需优化问题方面的优势。所引入的方法在一个由10台发电机组成的测试网络上的两个单独场景中进行了测试。使用本研究中建议的混合方法，解决了MADED和MADEED问题，并与其他进化技术（如原始WOA和PSO方法）进行了对比。对所提方法的结果进行检验表明，与其他方法相比，该方法具有更高的效率和更好的性能。最后，模拟得到的结果表明，纳入必要的系统约束赋予了系统合理性并产生了可靠的输出。关于从所引入方法获得的结果，与其他方法相比，总成本函数的值明显下降了约3%。
