Abstract. To estimate accurate voltage phasors from inaccurate voltage magnitude and complex power measurements, the standard approach is to iteratively refine a good initial guess using the Gauss–Newton method. But the nonconvexity of the estimation makes the Gauss–Newton method sensitive to its initial guess, so human intervention is needed to detect convergence to plausible but ultimately spurious estimates. This paper makes a novel connection between the angle estimation subproblem and phase synchronization to yield two key benefits: (1) an exceptionally high quality initial guess over the angles, known as a spectral initialization; (2) a correctness guarantee for the estimated angles, known as a global optimality certificate. These are formulated as sparse eigenvalue- eigenvector problems, which we efficiently compute in time comparable to a few Gauss-Newton iterations. Our experiments on the complete set of Polish, PEGASE, and RTE models show, where voltage magnitudes are already reasonably accurate, that spectral initialization provides an almost-perfect single-shot estimation of n angles from 2n moderately noisy bus power measurements (i.e. n pairs of PQ measurements), whose correctness becomes guaranteed after a single Gauss–Newton iteration. For less accurate voltage magnitudes, the performance of the method degrades gracefully; even with moderate voltage magnitude errors, the estimated voltage angles remain surprisingly accurate.

Abstract. Magnetic recording devices are still competitive in the storage density race with solid-state devices thanks to new technologies such as two-dimensional magnetic recording (TDMR). TDMR offers remarkable storage density increase without the need for new magnetic materials; however, advanced data processing schemes are needed to guarantee reliability. Data patterns where a bit is surrounded by complementary bits at the four positions with Manhattan distance 1 on the TDMR grid are called plus isolation (PIS) patterns, and they are error-prone. Recently, we introduced lexicographically-ordered constrained (LOCO) codes, namely optimal plus LOCO (OP-LOCO) codes, with minimal redundancy that prevent these patterns from being written in a TDMR device. However, in the high-density regime or the low-energy regime (as the device ages), additional error-prone patterns emerge, specifically data patterns where a bit is surrounded by complementary bits at only three positions with Manhattan distance 1, and we call them incomplete plus isolation (IPIS) patterns. In this paper, we present capacity-achieving codes that forbid both PIS and IPIS patterns in TDMR systems with wide read heads. Because of their shape, we collectively call the PIS and IPIS patterns rotated T isolation (RTIS) patterns, and we call the new codes optimal T LOCO (OT-LOCO) codes. We analyze OT-LOCO codes and derive their simple encoding-decoding rule that allows reconfigurability. We also present a novel bridging idea for these codes to further increase the rate. Our simulation results demonstrate that OT-LOCO codes not only remarkably outperform OP-LOCO codes, but also entirely eliminate media noise effects, resulting from error-prone data patterns, at practical TD densities in the range [0.6, 0.8) with high rates in the range [0.81, 0.83]. At the TD density of 0.8, the OT-LOCO code of rate 0.8267 achieves a frame error rate (bit error rate) performance gain of about 1.15 orders (1.23 orders) of magnitude for all TDMR down (horizontal) tracks compared with the uncoded setting. To further preserve the storage capacity, we suggest using OP-LOCO codes, which have higher rates than OT-LOCO codes, early in the device lifetime, then employing the reconfiguration property to switch to OT-LOCO codes later in the device lifetime. While the point of reconfiguration on the density/energy axis is decided manually at the moment, the next step is to use machine learning to make that decision based on the TDMR device status. Moreover, we introduce another coding scheme to remove RTIS patterns in TDMR systems which offers lower complexity, lower error propagation, and track separation, at the expense of a limited rate loss.

Abstract. Utilization of plug-in electric vehicle  (PEV) load models can improve the performance of smart charging strategies, which increase the reliability of the grid by harnessing the flexibility of PEV loads. This paper presents a method for utilizing personal PEV load models in real-time stochastic charging control with single and finite system-time horizons. First, the drivers’ load models are found with Kernel Density Estimation (KDE). Second, a single system-time horizon coordinated charging control algorithm is devised to ensure each PEV is charged at least a critical amount given a feasible set of optimization constraints. The coordinated charging algorithm tackles the NP-hardness of single-deadline charging scheduling problems efficiently with a sorting algorithm utilizing the stochastic PEV load models. Third, we extend the single system-time horizon coordinated charging control algorithm to a scheduling algorithm considering a finite system-time horizon. This approach utilizes the stochastic PEV load models in a model predictive control based approach to decrease the complexity of stochastic online charging scheduling problem into a deterministic case. The scheduling algorithm makes assumptions about the future arrivals to the charging station, unlike the classical online EV charging scheduling algorithms, which optimize the load demand revealed at the current time but underestimate the load demand revealed in the future. Our findings suggest the individual load models complement smart charging algorithms’ decision process by improving the fairness of charging time allocation and extending the degree of knowledge of future random data for the scheduling algorithm.

Abstract. The widespread adoption of plug-in electric vehicles (PEVs) is a path to be taken towards a green energy future, yet the uncoordinated penetration of PEVs prompts overloadings and low voltage violations that the existing power grid is not capable of managing. This issue can be addressed by utilizing PEV load models in component selection and smart charging strategies. PEV load modeling researches focus on the aggregator's and system operator's perspectives, and consideration of individual PEV loads in charging strategies tend to use generalized assumptions. However, consumers' perspectives should also be considered in real-time charging strategies. This paper presents a method to develop the individual load models of PEV users with Kernel Density Estimation (KDE) for smart charging strategies. A simulations section that compares uncoordinated, coordinated, and First Come First Serve (FCFS) charging approaches is presented. The results show that individual load models complement smart charging algorithms' decision process.

Abstract. The widespread adoption of plug-in electric vehicles (PEVs) is a path to be taken towards a green energy future, yet the uncoordinated penetration of PEVs prompts overloadings and low voltage violations that the existing power grid is not capable of managing. This issue can be addressed by utilizing PEV load models in component selection and smart charging strategies. PEV load modeling researches focus on the aggregator's and system operator's perspectives, and consideration of individual PEV loads in charging strategies tend to use generalized assumptions. However, consumers' perspectives should also be considered in real-time charging strategies. This paper presents a method to develop the individual load models of PEV users with Kernel Density Estimation (KDE) for smart charging strategies. A simulations section that compares uncoordinated, coordinated, and First Come First Serve (FCFS) charging approaches is presented. The results show that individual load models complement smart charging algorithms' decision process.