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Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators

机译：考虑到环境因素和发电机的物理限制，电力市场中发电机组的智能实时调度

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Optimal scheduling of generating resources plays a significant role as a decision-making tool for power system operators in the liberalized and real-time electricity spot markets. The real-time scheduling of generating units will become a very complex task with respect to the instantaneous fluctuation of the load demand due to several demand response scenarios in the smart grid context. In this study, a hybrid mathematical method for the online scheduling of units based on the least square support vector machine (LSSVM) and the third version of cultural algorithm (CA3) has been presented, where the CA3 has been specifically employed to tune the adjusting parameters of LSSVM. For the training purpose of the proposed method, the optimal scheduling of the daily load curve for four different test systems and various physical and environmental constraints of generating units have been prepared by using a modified mixed integer quadratic programming (MIQP) to deal with non-convex behaviors of the test systems. A mean squared error (MSE) objective function has been used to reduce the prediction errors during the training process to enhance the precision and reliability of the results. A radial basis function (RBF) and the proposed LSSVM-CA3 were used to check the convergence process. A high accuracy of generator schedule predictions are demonstrated by comparing the results of the proposed method with those of artificial neural networks. From the results, it can be inferred that the method is highly compatible for real-time dispatching of generation resources in deregulated electricity markets. (C) 2016 Elsevier Ltd. All rights reserved.
机译：最优的发电资源调度在开放和实时的电力现货市场中作为电力系统运营商的决策工具发挥着重要作用。由于智能电网环境中的几种需求响应场景，对于负荷需求的瞬时波动，发电机组的实时调度将成为一项非常复杂的任务。在这项研究中，提出了一种基于最小二乘支持向量机（LSSVM）和第三版文化算法（CA3）的在线设备在线调度的混合数学方法，其中，CA3被专门用于调整调整LSSVM的参数。出于所提方法的训练目的，通过使用改进的混合整数二次规划（MIQP）处理非标准机组的四个不同测试系统以及发电机组各种物理和环境约束的日负荷曲线的最佳调度，测试系统的凸行为。均方误差（MSE）目标函数已用于减少训练过程中的预测误差，以提高结果的准确性和可靠性。径向基函数（RBF）和提出的LSSVM-CA3用于检查收敛过程。通过将所提出的方法的结果与人工神经网络的结果进行比较，证明了发电机调度预测的高精度。从结果可以推断出，该方法与放松管制的电力市场中的发电资源的实时调度高度兼容。 （C）2016 Elsevier Ltd.保留所有权利。

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