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Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation

机译:使用增广拉格朗日松弛的具有传输和环境约束的短期发电调度

摘要

This paper proposes a new approach based on augmented Lagrangian relaxation for short term generation scheduling problem with transmission and environmental constraints. In this method, the system constraints, e.g. load demand, spinning reserve, transmission capacity and environmental constraints, are relaxed by using Lagrangian multipliers, and quadratic penalty terms associated with system load demand balance are added to the Lagrangian objective function. Then the decomposition and coordination technique is used, and non-separable quadratic penalty terms are replaced by linearization around the solution obtained from the previous iteration. In order to improve the convergence property, the exactly convex quadratic terms of decision variables are added to the objective function as strongly convex, differentiable and separable auxiliary functions. The overall problem is decomposed into N subproblems, multipliers and penalty coefficients are updated in the dual problem and system constraints are satisfied iteratively. The corresponding unit commitment subproblems are solved by dynamic programming, and the economic dispatch with transmission and environmental constraints is solved by an efficient network flow programming algorithm. The augmented Lagrangian relaxation method enhanced by decomposition and coordination technique avoids oscillations associated with piece-wise linear cost functions. Numerical results indicate that the proposed approach is fast and efficient in dealing with numerous system constraints.
机译:本文针对具有传输和环境约束的短期发电调度问题,提出了一种基于增强拉格朗日松弛的新方法。在这种方法中,系统约束例如负载需求,旋转储备,传输容量和环境约束可通过使用拉格朗日乘数来放松,并将与系统负载需求平衡相关的二次惩罚项添加到拉格朗日目标函数中。然后,使用分解和协调技术,并且围绕前次迭代获得的解决方案,将不可分离的二次惩罚项替换为线性化。为了提高收敛性,将决策变量的精确凸二次项作为强凸,可微和可分离的辅助函数添加到目标函数。将整个问题分解为N个子问题,在对偶问题中更新乘数和惩罚系数,并迭代地满足系统约束。通过动态规划解决了相应的机组承诺子问题,并通过高效的网络流规划算法解决了具有运输和环境约束的经济调度。通过分解和协调技术增强的增强拉格朗日松弛方法避免了与分段线性成本函数相关的振荡。数值结果表明,该方法在处理众多系统约束时是快速有效的。

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