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A new type of thermoacoustic engine based on traveling waves and ideally reversible heat transfer is described. Measurements and analysis of its performance are presented. This new engine outperforms previous thermoacoustic engines, which are based on standing waves and intrinsically irreversible heat transfer, by more than 50%. At its most efficient operating point, it delivers 710 W of acoustic power to its resonator with a thermal efficiency of 0.30, corresponding to 41% of the Carnot efficiency. At its most powerful operating point, it delivers 890 W to its resonator with a thermal efficiency of 0.22. The efficiency of this engine can be degraded by two types of acoustic streaming. These are suppressed by appropriate tapering of crucial surfaces in the engine and by using additional nonlinearity to induce an opposing time-averaged pressure difference. Data are presented which show the nearly complete elimination of the streaming convective heat loads. Analysis of these and other irreversibilities show which components of the engine require further research to achieve higher efficiency. Additionally, these data show that the dynamics and acoustic power flows are well understood, but the details of the streaming suppression and associated heat convection are only qualitatively understood.

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We consider experimentally the instability and mass transport of a porous-medium flow in a Hele-Shaw geometry. In an initially stable configuration, a lighter fluid (water) is located over a heavier fluid (propylene glycol). The fluids mix via diffusion with some regions of the resulting mixture being heavier than either pure fluid. Density-driven convection occurs with downward penetrating dense fingers that transport mass much more effectively than diffusion alone. We investigate the initial instability and the quasi steady state. The convective time and velocity scales, finger width, wave number selection, and normalized mass transport are determined for 6, 000 < Ra < 90, 000. The results have important implications for determining the time scales and rates of dissolution trapping of carbon dioxide in brine aquifers proposed as possible geologic repositories for sequestering carbon dioxide.

Abstract-We formulate the control of reactive power generation by photovoltaic inverters in a power distribution circuit as a constrained optimization that aims to minimize reactive power losses subject to finite inverter capacity and upper and lower voltage limits at all nodes in the circuit. When voltage variations along the circuit are small and losses of both real and reactive powers are small compared to the respective flows, the resulting optimization problem is convex. Moreover, the cost function is separable enabling a distributed, on-line implementation with node-local computations using only local measurements augmented with limited information from the neighboring nodes communicated over cyber channels. Such an approach lies between the fully centralized and local policy approaches previously considered. We explore protocols based on the dual ascent method and on the Alternating Direction Method of Multipliers (ADMM) and find that the ADMM protocol performs significantly better.Index Terms-photovoltaic power generation, reactive power control, power flow, ADMM, dual ascent method, distributed algorithms, distributed control.

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Abstract-Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a Robust Chance Constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. Deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.Index Terms-Wind power integration, power system economics, optimal power flow, wind power uncertainty, wind power variability, optimization methods, chance constrained optimization, distributionally robust optimization.

We show how distributed control of reactive power can serve to regulate voltage and minimize resistive losses in a distribution circuit that includes a significant level of photovoltaic (PV) generation. To demonstrate the technique, we consider a radial distribution circuit with a single branch consisting of sequentially-arranged residential-scale loads that consume both real and reactive power. In parallel, some loads also have PV generation capability. We postulate that the inverters associated with each PV system are also capable of limited reactive power generation or consumption, and we seek to find the optimal dispatch of each inverter's reactive power to both maintain the voltage within an acceptable range and minimize the resistive losses over the entire circuit. We assume the complex impedance of the distribution circuit links and the instantaneous load and PV generation at each load are known. We compare the results of the optimal dispatch with a suboptimal local scheme that does not require any communication. On our model distribution circuit, we illustrate the feasibility of high levels of PV penetration and a significant (20% or higher) reduction in losses.