Transmission Expansion Planning Considering Energy Storage

In electricity transmission networks, energy storage systems (ESS) provide a means of upgrade deferral by smoothing supply and matching demand. We develop a mixed integer programming (MIP) extension to the transmission network expansion planning (TEP) problem that considers the installation and operation of ESS as well as additional circuits. The model is demonstrated on the well known Garver's 6-bus and IEEE 25-bus test circuits for two 24 hour operating scenarios; a short peak, and a long peak. We show optimal location and capacity of storage is sensitive not only to cost, but also variability of demand in the network

Multi-Objective Mixed Integer Programming: An Objective Space Algorithm

This paper introduces the first objective space algorithm which can exactly find all supported and non-supported non-dominated solutions to a mixed-integer multi-objective linear program with an arbitrary number of objective functions. This algorithm is presented in three phases. First it builds up a super-set which contains the Pareto front. This super-set is then modified to not contain any intersecting polytopes. Once this is achieved, the algorithm efficiently calculates which portions of the super-set are not part of the Pareto front and removes them, leaving exactly the Pareto front.Comment: 6 pages, presented at LeGO International Global Optimization Workshop. At time of submission, no competing algorithm was known, but a competing algorithm was published between submission and presentation of this wor

Investigating the Role of Mineralocorticoid Receptor Signaling in Cancer Biology in the Genomic Era

In the last decades, advances that take place in the next-generation sequencing and bioinformatics research have helped reveal tissue- and cancer-specific gene expression patterns and mutation landscapes. Indeed, such data are now easily accessible via online genome browsers and different types and levels of public data compendia. Appropriate use of these tools eventually can lead to better patient stratification for diagnosis, prognosis, and therapy of cancers. Mineralocorticoid receptor (MR), encoded by NR3C2 gene, has long been implicated in the development and progression of multiple cancers. Nevertheless, MR has remained relatively understudied at the genomic and transcriptomic levels. In this review, we present the current, literature-based state of knowledge on the role of MR primarily in epithelial cancers. At the same time, we summarize the gene expression, mutation, and copy number variation data on MR obtained from The Cancer Genome Atlas (TCGA). We also show that MR expression could be a promising prognostic marker in different cancers using online tools for survival data analysis. Accordingly, this review strongly demonstrates the emerging potential of studying MR using available tools from the genomics/transcriptomics field for improving cancer diagnosis and prognostication

Computing the crosscap number of a knot using integer programming and normal surfaces

The crosscap number of a knot is an invariant describing the non-orientable surface of smallest genus that the knot bounds. Unlike knot genus (its orientable counterpart), crosscap numbers are difficult to compute and no general algorithm is known. We present three methods for computing crosscap number that offer varying trade-offs between precision and speed: (i) an algorithm based on Hilbert basis enumeration and (ii) an algorithm based on exact integer programming, both of which either compute the solution precisely or reduce it to two possible values, and (iii) a fast but limited precision integer programming algorithm that bounds the solution from above. The first two algorithms advance the theoretical state of the art, but remain intractable for practical use. The third algorithm is fast and effective, which we show in a practical setting by making significant improvements to the current knowledge of crosscap numbers in knot tables. Our integer programming framework is general, with the potential for further applications in computational geometry and topology.Comment: 19 pages, 7 figures, 1 table; v2: minor revisions; to appear in ACM Transactions on Mathematical Softwar

An optimisation approach for fuel treatment planning to break the connectivity of high-risk regions

Uncontrolled wildfires can lead to loss of life and property and destruction of natural resources. At the same time, fire plays a vital role in restoring ecological balance in many ecosystems. Fuel management, or treatment planning by way of planned burning, is an important tool used in many countries where fire is a major ecosystem process. In this paper, we propose an approach to reduce the spatial connectivity of fuel hazards while still considering the ecological fire requirements of the ecosystem. A mixed integer programming (MIP) model is formulated in such a way that it breaks the connectivity of high-risk regions as a means to reduce fuel hazards in the landscape. This multi-period model tracks the age of each vegetation type and determines the optimal time and locations to conduct fuel treatments. The minimum and maximum Tolerable Fire Intervals (TFI), which define the ages at which certain vegetation type can be treated for ecological reasons, are taken into account by the model. Examples from previous work that explicitly disconnect contiguous areas of high fuel load have often been limited to using single vegetation types implemented within rectangular grids. We significantly extend such work by including modelling multiple vegetation types implemented within a polygon-based network to achieve a more realistic representation of the landscape. An analysis of the proposed approach was conducted for a fuel treatment area comprising 711 treatment units in the Barwon-Otway district of Victoria, Australia. The solution of the proposed model can be obtained for 20-year fuel treatment planning within a reasonable computation time of eight hours