ASU Scholarship Showcase

Transmission systems are under stress and need to be upgraded. Better utilization of the existing grid provides a fast and cheap alternative to building new transmission. One way to improve the utilization of the transmission network is power flow control via flexible AC transmission system (FACTS) devices. While FACTS devices are used today, the utilization of these devices is limited; traditional dispatch models (e.g., security con-strained economic dispatch) assume a fixed, static transmission grid even though it is rather flexible. The primary barrier is the complexity that is added to the power flow problem. The mathe-matical representation of the DC optimal power flow, with the added modeling of FACTS devices, is a nonlinear program (NLP). This paper presents a method to convert this NLP into a mixed-integer linear program (MILP). The MILP is reformulat-ed as a two-stage linear program, which enforces the same sign for the voltage angle differences for the lines equipped with FACTS. While this approximation does not guarantee optimality, more than 98% of the presented empirical results, based on the IEEE 118 bus and Polish system, achieved global optimality. In the case of suboptimal solutions, the savings were still significant and the solution time was dramatically reduced.

Reserve requirements serve as a proxy for N-1 reli-ability in the security-constrained unit commitment (SCUC) problem. However, there is no guarantee that the reserve is deliv-erable for all scenarios (post-contingency states). One cheap way to improve reserve deliverability is to harness the flexibility of the transmission network. Flexible AC transmission system (FACTS) devices are able to significantly improve the transfer capability. However, FACTS utilization is limited today due to the complexi-ties these devices introduce to the DC optimal power flow prob-lem (DCOPF). With a linear objective, the traditional DCOPF is a linear program (LP); when variable impedance based FACTS devices are taken into consideration, the problem becomes a non-linear program (NLP). A reformulation of the NLP to a mixed integer linear program, for day-ahead corrective operation of FACTS devices, is presented in this paper. Engineering insight is then introduced to further reduce the complexity to an LP. Alt-hough optimality is not guaranteed, the simulation studies on the IEEE 118-bus system show that the method finds the globally optimal solution in 98.8% of the cases. Even when the method did not find the optimal solution, it was able to converge to a near-optimal solution, which substantially improved the reliability, very quickly.

Transmission switching (TS) has shown to be an ef-fective power flow control tool. TS can reduce the system cost, improve system reliability, and enhance the management of in-termittent renewable resources. This paper addresses the state of the art problem of TS by developing an AC-based real-time con-tingency analysis (RTCA) package with TS. The package is tested on real power system data, taken from energy management sys-tems of PJM, TVA, and ERCOT. The results show that post-contingency corrective switching is a ready to be implemented transformational technology that provides substantial reliability gains. The computational time and the performance of the devel-oped RTCA package, reported in the paper, are promising.

Meeting time-varying peak demand poses a key challenge to the U.S. electricity system. Building-based electricity storage – to enable demand response (DR) without curtailing actual appliance usage – offers potential benefits of lower electricity production cost, higher grid reliability, and more flexibility to integrate renewables. DR tariffs are currently available in the U.S. but building-based storage is still underutilized due to insufficiently understood cost-effectiveness and dispatch strategies. Whether DR schemes can yield a profit for building operators (i.e., reduction in electricity bill that exceeds levelized storage cost) and which particular storage technology yields the highest profit is yet to be answered. This study aims to evaluate the economics of providing peak shaving DR under a realistic tariff (Con Edison, New York), using a range of storage technologies (conventional and advanced batteries, flywheel, magnetic storage, pumped hydro, compressed air, and capacitors). An agent-based stochastic model is used to randomly generate appliance-level demand profiles for an average U.S. household. We first introduce a levelized storage cost model which is based on a total-energy-throughput lifetime. We then develop a storage dispatch strategy which optimizes the storage capacity and the demand limit on the grid. We find that (i) several storage technologies provide profitable DR; (ii) annual profit from such DR can range from 1% to 39% of the household’s non-DR electricity bill; (iii) allowing occasional breaches of the intended demand limit increases profit; and (iv) a dispatch strategy that accounts for demand variations across seasons increases profit further. We expect that a more advanced dispatch strategy with embedded weather forecasting capability could yield even higher profit.

New and important separations capabilities are being enabled by utilizing other electric field-induced forces besides electrophoresis, among these is dielectrophoresis. Recent works have used experimentally simple insulator-based systems that induce field gradients creating dielectrophoretic force in useful formats. Among these, juxtaposing forces can generate gradient-based steady-state separations schemes globally similar to isoelectric focusing. The system of interest is termed gradient insulator-based dielectrophoresis and can create extremely high resolution steady-state separations for particles four nanometers to ten micrometers in diameter, including nearly all important bioparticles (large proteins, protein aggregates, polynucleotides viruses, organelles, cells, bacteria, etc.). A theoretical underpinning is developed here to understand the relationship between experimental parameters and resolution and to identify the best expected resolution possible. According to the results, differences in particles (and bioparticles) as small as one part in 10⁴ for diameter (subnanometer resolution for a one micrometer particle), one part in 10⁸ for dielectrophoretic parameters (dielectrophoretic mobility, Clausius-Mossotti factor), and one part in 10⁵ for electrophoretic mobility can be resolved. These figures of merit are generally better than any competing technique, in some cases by orders of magnitude. This performance is enabled by very strong focusing forces associated with localized gradients.

The impetus for discovery and evaluation of protein biomarkers has been accelerated by recent development of advanced technologies for rapid and broad proteome analyses. Mass spectrometry (MS)-based protein assays hold great potential for in vitro biomarker studies. Described here is the development of a multiplex mass spectrometric immunoassay (MSIA) for quantification of apolipoprotein C-I (apoC-I), apolipoprotein C-II (apoC-II), apolipoprotein C-III (apoC-III) and their proteoforms. The multiplex MSIA assay was fast (∼40 min) and high-throughput (96 samples at a time). The assay was applied to a small cohort of human plasma samples, revealing the existence of multiple proteoforms for each apolipoprotein C. The quantitative aspect of the assay enabled determination of the concentration for each proteoform individually. Low-abundance proteoforms, such as fucosylated apoC-III, were detected in less than 20% of the samples. The distribution of apoC-III proteoforms varied among samples with similar total apoC-III concentrations. The multiplex analysis of the three apolipoproteins C and their proteoforms using quantitative MSIA represents a significant step forward toward better understanding of their physiological roles in health and disease.