In gas transmission networks, the pressure drop caused by friction is one of the main operation costs that is compensated through consuming energy in the compressors. In the competitive market of energy, considering the demand variation is inevitable. Hence, the power minimization should be carried out in transient state. Since the minimization problem is severely nonlinear and nonconvex subjected to nonlinear constraints, utilizing a powerful minimization tool with a straightforward procedure is very helpful. In this paper, a novel approach is proposed based on metaheuristic algorithms for power minimization of a gas transmission network in fully transient conditions. The metaheuristic algorithms, unlike the gradient dependent method, can solve the complicated minimization problem without simplification. In the proposed strategy, the cost function is not expressed explicitly as a function of minimization variables; therefore, the transient minimization can be as precise as possible. The minimization is carried out by a straightforward methodology in each time sample, which leads to more precise solutions as compared to the quasi transient minimization. The metaheuristic minimizer, called the particle swarm optimization gravitational search algorithm (PSOGSA), is utilized to find the optimum operating set points. The numerical results also confirm the accuracy and well efficiency of the proposed method.

中文翻译：

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使用元启发式方法在完全瞬态下最小化气体传输网络的功率

在气体传输网络中，由摩擦引起的压降是主要的运营成本之一，通过消耗压缩机中的能量来补偿。在竞争激烈的能源市场中，考虑到需求的变化是不可避免的。因此，功率最小化应该在瞬态下进行。由于最小化问题是严重非线性且非凸的，因此受到非线性约束，因此使用功能强大的最小化工具和简单的程序是非常有帮助的。在本文中，提出了一种基于元启发式算法的新方法，用于在完全瞬态条件下实现天然气传输网络的功率最小化。与梯度相关的方法不同，元启发式算法可以在不简化的情况下解决复杂的最小化问题。在提议的战略中，成本函数没有明确表示为最小化变量的函数；因此，瞬态最小化可以尽可能精确。最小化是在每个时间样本中通过简单的方法进行的，与准瞬态最小化相比，这会导致更精确的解决方案。元启发式最小化器，称为粒子群优化引力搜索算法 (PSOGSA)，用于找到最佳操作设定点。数值结果也证实了所提出方法的准确性和良好的效率。与准瞬态最小化相比，这导致更精确的解决方案。元启发式最小化器，称为粒子群优化引力搜索算法 (PSOGSA)，用于找到最佳操作设定点。数值结果也证实了所提出方法的准确性和良好的效率。与准瞬态最小化相比，这导致更精确的解决方案。元启发式最小化器，称为粒子群优化引力搜索算法 (PSOGSA)，用于找到最佳操作设定点。数值结果也证实了所提出方法的准确性和良好的效率。