Mixed integer nonlinear programming for Joint Coordination of Plug-in Electrical Vehicles Charging and Smart Grid Operations

The problem of joint coordination of plug-in electric vehicles (PEVs) charging and grid power control is to minimize both PEVs charging cost and energy generation cost while meeting both residential and PEVs' power demands and suppressing the potential impact of PEVs integration. A bang-bang PEV charging strategy is adopted to exploit its simple online implementation, which requires computation of a mixed integer nonlinear programming problem (MINP) in binary variables of the PEV charging strategy and continuous variables of the grid voltages. A new solver for this MINP is proposed. Its efficiency is shown by numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0445

Model Predictive Control for Smart Grids with Multiple Electric-Vehicle Charging Stations

Next-generation power grids will likely enable concurrent service for residences and plug-in electric vehicles (PEVs). While the residence power demand profile is known and thus can be considered inelastic, the PEVs' power demand is only known after random PEVs' arrivals. PEV charging scheduling aims at minimizing the potential impact of the massive integration of PEVs into power grids to save service costs to customers while power control aims at minimizing the cost of power generation subject to operating constraints and meeting demand. The present paper develops a model predictive control (MPC)- based approach to address the joint PEV charging scheduling and power control to minimize both PEV charging cost and energy generation cost in meeting both residence and PEV power demands. Unlike in related works, no assumptions are made about the probability distribution of PEVs' arrivals, the known PEVs' future demand, or the unlimited charging capacity of PEVs. The proposed approach is shown to achieve a globally optimal solution. Numerical results for IEEE benchmark power grids serving Tesla Model S PEVs show the merit of this approach

Cartilage stem/progenitor cells are activated in osteoarthritis via interleukin-1β/nerve growth factor signaling

Introduction: Interleukin-1β (IL-1β) and nerve growth factor (NGF) are key regulators in the pathogenesis of inflammatory arthritis; specifically, IL-1β is involved in tissue degeneration and NGF is involved in joint pain. However, the cellular and molecular interactions between IL-1β and NGF in articular cartilage are not known. Cartilage stem/progenitor cells (CSPCs) have recently been identified in osteoarthritic (OA) cartilage on the basis of their migratory properties. Here we hypothesize that IL-1β/NGF signaling is involved in OA cartilage degeneration by targeting CSPCs. Method: NGF and NGF receptor (NGFR: TrkA and p75NTR) expression in healthy and OA human articular cartilage and isolated chondrocytes was determined by immunostaining, qRT-PCR, flow cytometry and western blot. Articular cartilage derived stem/progenitor cells were collected and identified by stem/progenitor cell characteristics. 3D-cultured CSPC pellets and cartilage explants were treated with NGF and NGF neutralizing antibody, and extracellular matrix changes were examined by sulfated glycosaminoglycan (GAG) release and MMP expression and activity. Results: Expression of NGF, TrkA and p75NTR was found to be elevated in human OA cartilage. Cellular changes upon IL-1β and/or NGF treatment were then examined. NGF mRNA and NGFR proteins levels were upregulated in cultured chondrocytes exposed to IL-1β. NGF was chemotactic for cells isolated from OA cartilage. Cells isolated on the basis of their chemotactic migration towards NGF demonstrated stem/progenitor cell characteristics, including colony-forming ability, multi-lineage differentiation potential, and stem cell surface markers. The effects of NGF perturbation in cartilage explants and 3D-cultured CSPCs were next analyzed. NGF treatment resulted in extracellular matrix catabolism indicated by increased sGAG release and MMP expression and activity; conversely, treatment with NGF neutralizing antibody inhibited increased MMP levels, and enhanced tissue inhibitor of matrix metalloprotease-1 (TIMP1) expression in OA cartilage explants. NGF blockade with neutralizing antibody also affected cartilage matrix remodeling in 3D-CSPC pellet cultures. Conclusion: Our results strongly suggest that NGF signaling is a contributing factor in articular cartilage degeneration in OA, which likely targets a specific subpopulation of progenitor cells, the CSPCs, affecting their migratory and matrix remodeling activities. These findings provide novel cellular/signaling therapeutic targets in osteoarthritic cartilage

The fluctuation energy balance in non-suspended fluid-mediated particle transport

Here we compare two extreme regimes of non-suspended fluid-mediated particle transport, transport in light and heavy fluids ("saltation" and "bedload", respectively), regarding their particle fluctuation energy balance. From direct numerical simulations, we surprisingly find that the ratio between collisional and fluid drag dissipation of fluctuation energy is significantly larger in saltation than in bedload, even though the contribution of interparticle collisions to transport of momentum and energy is much smaller in saltation due to the low concentration of particles in the transport layer. We conclude that the much higher frequency of high-energy particle-bed impacts ("splash") in saltation is the cause for this counter-intuitive behavior. Moreover, from a comparison of these simulations to Particle Tracking Velocimetry measurements which we performed in a wind tunnel under steady transport of fine and coarse sand, we find that turbulent fluctuations of the flow produce particle fluctuation energy at an unexpectedly high rate in saltation even under conditions for which the effects of turbulence are usually believed to be small

Robust H-infinity filtering for 2-D systems with intermittent measurements

This paper is concerned with the problem of robust H∞ filtering for uncertain two-dimensional (2-D) systems with intermittent measurements. The parameter uncertainty is assumed to be of polytopic type, and the measurements transmission is assumed to be imperfect, which is modeled by a stochastic variable satisfying the Bernoulli random binary distribution. Our attention is focused on the design of an H∞ filter such that the filtering error system is stochastically stable and preserves a guaranteed H∞ performance. This problem is solved in the parameter-dependent framework, which is much less conservative than the quadratic approach. By introducing some slack matrix variables, the coupling between the positive definite matrices and the system matrices is eliminated, which greatly facilitates the filter design procedure. The corresponding results are established in terms of linear matrix inequalities, which can be easily tested by using standard numerical software. An example is provided to show the effectiveness of the proposed approac