This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

中文翻译：

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多水库系统实时运行的多目标模型预测控制

本文提出了一种扩展的模型预测控制方案，称为多目标模型预测控制 (MOMPC)，用于多水库系统的实时操作。MOMPC 方法结合了非支配排序遗传算法 II (NSGA-II)、多准则决策 (MCDM) 和后退层原理来实时解决多目标油藏作业问题。在本研究中，使用 De Saint Venant 方程和结构流动方程来模拟水系统。为解决多目标优化问题，NSGA-II 用于寻找冲突目标的帕累托最优解，并基于多个标准做出控制决策。应用于缅甸实当河流域的现有水库系统，需要优化操作来折衷三个操作目标。控制目标是最大限度地减少水库的蓄水偏差，最大限度地减少下游脆弱地区的洪水风险，并最大限度地提高水力发电量。在找到一组候选解决方案后，使用几个决策规则来访问系统的整体性能。此外，还讨论了不同决策方法的影响。结果表明，MOMPC 方法适用于支持决策者实时运行多水库系统。一些决策规则用于访问系统的整体性能。此外，还讨论了不同决策方法的影响。结果表明，MOMPC 方法适用于支持决策者实时运行多水库系统。一些决策规则用于访问系统的整体性能。此外，还讨论了不同决策方法的影响。结果表明，MOMPC 方法适用于支持决策者实时运行多水库系统。