Stochastic Optimization of Power System Dynamics for Grid Resilience.
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For spacecraft operation in the near Earth environment, solar cell arrays constitute the major source of reliable long term power. Optimization of mass and power efficiency results in a general requirement for high voltage solar arrays. The space plasma environment, though, can result in large currents being collected by exposed solar cells. The solution of a protective covering of transparent insulation is not a complete solution, inasmuch as defects in the insulation result in anomalously large currents being collected through the defects. Tests simulating the electron collection from small defects in an insulation have shown that there are two major collection modes. The first mode involves current enhancement by means of a surface phenomenon involving the surrounding insulator. In the second mode the current collection is enhanced by vaporization and ionization of the insulators materials, in addition to the surface enhancement of the first mode. A model for the electron collection is the surface enhanced collection mode was developed. The model relates the secondary electron emission yield to the electron collection. It correctly predicts the qualitative effects of hole size, sample temperature and roughening of sample surface. The theory was also shown to predict electron collection within a factor of two for the polymers teflon and polyimide.
JBK-75 is an iron-nickel derivative alloy of A-286 that has been of interest to NASA’s propulsion community for use in fuel injectors and other components that are used in hot, corrosive environments. To enable the rapid production of these components in JBK-75, Marshall Space Flight Center has developed parameters for manufacturing fully dense JBK-75 components using powder bed fusion additive manufacturing (PBFAM). These parameters were developed in a two-step development. First, the depth of laser penetration in the powdered material was measured across a spectrum of laser powers to determine the optimal power necessary for generating the desired meltpool depth. The parameter set’s vector-to-vector spacing was then then tailored to guarantee the full densification of the desired area. The results of this development was a readily implementable parameter that produced 99.6% dense material when using a 147W power running at 600mm/s with a 85μm(32%) vector-to-vector spacing.
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The nonlinear programming (NLP) problem to solve distribution-level optimal power flow (D-OPF) poses convergence issues and does not scale well for unbalanced distribution systems. The existing scalable D-OPF algorithms either use approximations that are not valid for an unbalanced power distribution system, or apply relaxation techniques to the nonlinear power flow equations that do not guarantee a feasible power flow solution. In this paper, we propose scalable D-OPF algorithms that simultaneously achieve optimal and feasible solutions by solving multiple iterations of approximate, or relaxed, D-OPF subproblems of low complexity. The first algorithm is based on a successive linear approximation of the nonlinear power flow equations around the current operating point, where the D-OPF solution is obtained by solving multiple iterations of a linear programming (LP) problem. The second algorithm is based on the relaxation of the nonlinear power flow equations as conic constraints together with directional constraints, which achieves optimal and feasible solutions over multiple iterations of a second-order cone programming (SOCP) problem. Finally, it is demonstrated that the proposed algorithms are able to reach an optimal and feasible solution while significantly reducing the computation time as compared to an equivalent NLPD-OPF model for the same distribution system.
A demand bidding mechanism for engaging large industrial electricity users in the operation of the power grid is presented. Demand bidding is formulated as an optimization problem based on a modified version of the alternating current optimal power flow problem, and can be interpreted as a tâtonnement process between the grid operator and electricity users. Here, the work provides the first – to the authors’ knowledge – grid-scale case study of demand bidding, using a synthetic grid structure in the footprint of the grid of Texas. Results reveal that the demand bidding lowers overall power generation costs, but economic benefits plateau as the number of participants increases. Transmission line and transformer capacity constraints become the limiting factors, revealing that expanding and fortifying the transmission infrastructure is key to expanding demand-side participation. Demand bidding does not substantially alter the optimal operation of existing bidding entities when the number of bidders increases, thereby supporting existing bidders to stay in the system and encouraging new ones to join.