Existing electrical grid systems have a limited amount of real-time monitoring and controlling capabilities of energy generation and consumption facilities, which trigger various technical issues including voltage overloading, demand_supply mismatch, peak load consumption, etc. Some of the primary reasons for these key issues have been identified to be the inefficient utilization of energy infrastructure and uncoordinated power consumption pattern among the consumers. In this paper, we propose a coordinated load scheduling and controlling algorithm to schedule controllable appliances with the objective to minimize peak load consumption. For this purpose, we model the problem into the strip packing problem, a well-known NP-hard problem, and discuss the applicability of existing heuristics in our problem setup. We then discuss a new offline heuristic solution, named MinPeak, specifically designed for load scheduling problem. We have conducted comprehensive simulation studies using available benchmark data sets and have performed extensive comparative analyses of the proposed algorithm with some of the well-known heuristics for strip packing problem. Furthermore, experiments have been carried out using practical electricity consumption data to evaluate the performance of the algorithm in real life. The results obtained are very encouraging in terms of reducing peak load consumption and overall efficiency of the system.

中文翻译：

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用于智能电网峰值负载优化的非抢占式设备的高效调度


现有电网系统对能源生产和用电设施的实时监控能力有限，引发电压过载、供需不匹配、高峰用电等技术问题。造成这些关键问题的一些主要原因已确定的问题是能源基础设施利用效率低下和消费者之间的用电模式不协调。在本文中，我们提出了一种协调负载调度和控制算法来调度可控设备，以最大限度地减少峰值负载消耗。为此，我们将问题建模为条带堆积问题（一个众所周知的 NP 难问题），并讨论现有启发式方法在我们的问题设置中的适用性。然后我们讨论一种新的离线启发式解决方案，名为 MinPeak，专门针对负载调度问题而设计。我们使用可用的基准数据集进行了全面的模拟研究，并对所提出的算法与一些众所周知的条带包装问题启发式算法进行了广泛的比较分析。此外，还利用实际用电量数据进行了实验，以评估算法在现实生活中的性能。就降低峰值负载消耗和系统整体效率而言，所获得的结果非常令人鼓舞。