Power generation planning (PGP) is a vigorous stage in power system planning subsequently forecasting the load demand. The nature of the PGP problem is highly constrained, dynamic, discrete, nonlinear, stochastic, and mixed-integer. The amalgamation of the renewable energy sources in strategic PGP generates the complexity in terms of feeble reliability because of variability. The meta-heuristic algorithms are referred to as efficient solution techniques for the optimization of challenging and minimum-cost PGP. In this paper, a novel PGP optimizer has been proposed to plan the power generation plants reflecting the minimum cost and attaining an explicit reliability evaluation for specific planning prospect. The formulated and proposed optimizer is called teaching–learning-based optimization employing correction matrix method-with-indicators (TLBO-CMMI). In TLBO-CMMI, a new radix-5-based mapping procedure has been developed for the representation of population search agents. An innovative constraint handling procedure known as correction matrix method-with-indicators and incremental cost-based strategy to compute variable operation and maintenance cost, have been embedded from our previous research work. The optimizer has been employed to perform the PGP for different test scenarios in the literature including reliability and emission constraints. The optimizer gives promising results considering minimum cost and computational time when compared with results by current methods in the literature. The suitability of the optimizer has also been assessed by applying to an actual scenario of the Pakistan power system to create a viable power generation plan.

中文翻译：

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一种基于混合教学的优化器，具有新颖的基数 5 映射程序，用于考虑可再生能源和减少排放的最低成本发电规划

发电规划 (PGP) 是电力系统规划中随后预测负荷需求的一个充满活力的阶段。PGP 问题的本质是高度约束、动态、离散、非线性、随机和混合整数。由于可变性，战略性 PGP 中可再生能源的合并产生了微弱可靠性方面的复杂性。元启发式算法被称为用于优化具有挑战性和最小成本的 PGP 的有效解决方案技术。在本文中，提出了一种新颖的 PGP 优化器来规划发电厂，以反映最低成本并针对特定规划前景获得明确的可靠性评估。制定和提出的优化器称为基于教学的优化，采用带有指标的校正矩阵方法 (TLBO-CMMI)。在 TLBO-CMMI 中，已经开发了一种新的基于 radix-5 的映射程序来表示人口搜索代理。我们之前的研究工作已经嵌入了一种创新的约束处理程序，称为带有指标的校正矩阵方法和基于增量成本的策略来计算可变运营和维护成本。优化器已被用于针对包括可靠性和排放约束在内的文献中的不同测试场景执行 PGP。与文献中当前方法的结果相比，优化器在考虑最低成本和计算时间的情况下给出了有希望的结果。