Cost Surfaces are a quantitative means of assigning social, environmental, and engineering costs that impact movement across landscapes. Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in a wide range of disciplines including computer science, landscape ecology, and energy infrastructure modeling. Linear features present a key weakness to traditional routing calculations along costs surfaces because they cannot identify whether moving from a cell to its adjacent neighbors constitutes crossing a linear barrier (increased cost) or following a corridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. In this paper, we introduce an approach to address this adjacency issue using a search kernel that identifies these critical barriers and corridors. We have built this approach into a new Java-based open-source software package-CostMAP (cost surface multilayer aggregation program)-which calculates cost surfaces and cost networks using the search kernel. CostMAP not only includes the new "adjacency" capability, it is also a versatile multi-platform package that allows users to input multiple GIS data layers and to set weights and rules for developing a weighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains-both following corridors and avoiding barriers-using examples in a movement ecology framework and pipeline routing for carbon capture, and storage (CCS). We also demonstrate that the new software can straightforwardly calculate cost surfaces on a national scale.
This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.
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