PDF(Pubmed)
Abstract：
The unit commitment (UC) optimization issue is a vital issue in the operation and management of power systems. In recent years, the significant inroads of renewable energy (RE) resources, especially wind power and solar energy generation systems, into power systems have led to a huge increment in levels of uncertainty in power systems. Consequently, solution the UC is being more complicated. In this work, the UC problem solution is addressed using the Artificial Gorilla Troops Optimizer (GTO) for three cases including solving the UC at deterministic state, solving the UC under uncertainties of system and sources with and without RE sources. The uncertainty modelling of the load and RE sources (wind power and solar energy) are made through representing each uncertain variable with a suitable probability density function (PDF) and then the Monte Carlo Simulation (MCS) method is employed to generate a large number of scenarios then a scenario reduction technique known as backward reduction algorithm (BRA) is applied to establish a meaningful overall interpretation of the results. The results show that the overall cost per day is reduced from 0.2181% to 3.7528% at the deterministic state. In addition to that the overall cost reduction per day is 19.23% with integration of the RE resources. According to the results analysis, the main findings from this work are that the GTO is a powerful optimizer in addressing the deterministic UC problem with better cost and faster convergence curve and that RE resources help greatly in running cost saving. Also uncertainty consideration makes the system more reliable and realistic.
摘要：
机组组合（UC）优化问题是电力系统运行和管理中的重要问题。近年来,可再生能源(RE)资源的重大侵入，特别是风力发电和太阳能发电系统，进入电力系统导致了电力系统不确定性水平的巨大增加。因此,解决方案UC变得更加复杂。在这项工作中,UC问题解决方案使用人工大猩猩部队优化器（GTO）解决了三种情况，包括在确定性状态下解决UC，在有和没有RE源的系统和源的不确定性下求解UC。通过用合适的概率密度函数（PDF）表示每个不确定变量，对负载和RE源（风能和太阳能）进行不确定性建模，然后采用蒙特卡罗模拟（MCS）方法生成大量场景，然后应用称为后向减少算法（BRA）的场景减少技术来建立对结果的有意义的总体解释。结果表明，在确定性状态下，每天的总成本从0.2181%降低到3.7528%。此外，通过整合可再生能源资源，每天的总成本降低了19.23％。根据结果分析,这项工作的主要发现是，GTO是解决确定性UC问题的强大优化器，具有更好的成本和更快的收敛曲线，并且RE资源极大地有助于节省运行成本。此外，不确定性的考虑使系统更加可靠和现实。
