Shaving Peaks by Augmenting the Dependency Graph
Shaving Peaks by Augmenting the Dependency Graph
复制标题
通过增强依赖图来削减峰值
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
D. Wagner
中科院分区：
文献类型：
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作者：
L. Barth;D. Wagner
需求侧管理(DSM)是未来能源系统的重要组成部分，因为它缓解了可再生能源发电不可调度、波动的问题。为了大规模实施集中式电力需求管理，必须以高时间分辨率快速调度大量电力需求。为此，我们提出了增广图启发式调度算法。Swg在作业依赖图上使用简单、高效的图形操作来优化具有高峰修剪目标的时间表。基于图的方法使其独立于时间解析，并以一种自然的方式合并作业依赖关系。在对算法的详细评估中，将SWIG与由混合整数程序计算的最优解进行了比较。在一组基于真实词汇消费数据的实例上，将Swg与另一种最先进的启发式算法进行比较，表明Swg的性能优于竞争对手，特别是在硬实例上。
Demand Side Management (DSM) is an important building block for future energy systems, since it mitigates the non-dispatchable, fluctuating power generation of renewables. For centralized DSM to be implemented on a large scale, considerable amounts of electrical demands must be scheduled rapidly with high time resolution. To this end, we present the Scheduling With Augmented Graphs (SWAG) heuristic. SWAG uses simple, efficient graph operations on a job dependency graph to optimize schedules with a peak shaving objective. The graph-based approach makes it independent of the time resolution and incorporates job dependencies in a natural way. In a detailed evaluation of the algorithm, SWAG is compared to optimal solutions computed by a mixed-integer program. A comparison of SWAG to another state-of-the-art heuristic on a set of instances based on real-word consumption data demonstrates that SWAG outperforms this competitor, in particular on hard instances.