Syama S, Ramprabhakar J, Anand R, Guerrero Josep M
Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Center for Research on Microgrids (UPC CROM), Department of Electronic Engineering, Technical University of Catalonia, 08019, Barcelona, Spain.
Sci Rep. 2024 Oct 14;14(1):23964. doi: 10.1038/s41598-024-75743-0.
The current generation portfolio is obligated to incorporate zero-emissions energy sources, predominantly wind and solar, due to the depletion of fossil fuels and the alarming rate of global warming. In the current scenario, power engineers must devise a compromised solution that not only advocates for the adoption of renewable energy sources (RES) but also efficiently schedules all conventional power generation units to balance the increasing load demand while simultaneously minimizing fuel costs and harmful emissions that are currently addressed by Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) problem solutions. However, the integration of renewable energy resources (RES) further complicates the UC-CEED problem due to their intermittent nature. Recently, metaheuristic algorithms are acquiring momentum in resolving constrained UC-CEED problems due to their improved global solution ability, adaptability, and derivative-free construction. In this research, a computationally efficient binary hybrid version of crow search algorithm and improvised grey wolf optimization is proposed, namely Crow Search Improved Binary Grey Wolf Optimization Algorithm (CS-BIGWO) by inclusion of nonlinear control parameter, weight-based position updating, and mutation approach. Statistical results on standard mathematical functions prove the supremacy of the proposed algorithm over conventional algorithms. Further, a novel optimization strategy is devised by integrating enhanced lambda iteration with the CS-BIGWO algorithm (CS-BIGWO- ) to solve a day-ahead UC-CEED problem of the hybrid energy system incorporating cost functions of RES. For the model, a day-ahead forecast of wind power and solar photovoltaic power is obtained by using the Levy-Flight Chaotic Whale Optimization Algorithm optimized Extreme Learning Machines(LCWOA-ELM). The proposed algorithm is tested for the UC-CEED solution of an IEEE-39 bus system with two distinct cases: (1) without RES integration and (2) with RES integration. Several independent trial runs are executed, and the performance of the algorithms is assessed based on optimal UC schedules, fuel cost, emission quantization, convergence curve, and computational time. For case 1, the proposed algorithm resulted in a percentage reduction of 0.1021% in fuel cost and 0.7995% in emission. In contrast, for test case 2, it resulted in a percentage reduction of 0.12896% in fuel cost and 0.772% in emission with the proposed algorithm. The results validate the dominance of the proposed methodology over existing methods in terms of lower fuel costs and emissions.
由于化石燃料的枯竭和全球变暖的惊人速度，当前一代的能源组合有义务纳入零排放能源，主要是风能和太阳能。在当前情况下，电力工程师必须设计出一种折中的解决方案，不仅要提倡采用可再生能源（RES），还要有效地调度所有传统发电单元，以平衡不断增长的负荷需求，同时将燃料成本和有害排放降至最低，而目前这些问题是通过机组组合（UC）和联合经济排放调度（CEED）问题解决方案来解决的。然而，由于可再生能源（RES）的间歇性，其整合进一步使UC-CEED问题复杂化。最近，元启发式算法因其改进的全局求解能力、适应性和无导数结构，在解决约束UC-CEED问题方面获得了发展势头。在本研究中，通过纳入非线性控制参数、基于权重的位置更新和变异方法，提出了一种计算效率高的乌鸦搜索算法和改进的灰狼优化的二进制混合版本，即乌鸦搜索改进二进制灰狼优化算法（CS-BIGWO）。关于标准数学函数的统计结果证明了所提算法优于传统算法。此外，通过将增强的λ迭代与CS-BIGWO算法（CS-BIGWO- ）相结合，设计了一种新颖的优化策略，以解决包含RES成本函数的混合能源系统的日前UC-CEED问题。对于该模型，通过使用莱维飞行混沌鲸鱼优化算法优化的极限学习机（LCWOA-ELM）获得了风电和太阳能光伏发电的日前预测。所提算法针对IEEE-39母线系统的UC-CEED解决方案在两种不同情况下进行了测试：（1）不整合RES和（2）整合RES。进行了多次独立试验运行，并根据最优UC调度、燃料成本、排放量化、收敛曲线和计算时间评估算法的性能。对于案例1，所提算法使燃料成本降低了0.1021%，排放降低了0.7995%。相比之下，对于测试案例2，所提算法使燃料成本降低了0.12896%，排放降低了0.772%。结果验证了所提方法在降低燃料成本和排放方面优于现有方法。
