The OSIRIS-REx mission collected a sample from the surface of the asteroid (101955) Bennu in 2020 October. Here, we study the impact of the OSIRIS-REx Touch-and-Go Sampling Acquisition Mechanism (TAGSAM) interacting with the surface of an asteroid in the framework of granular physics. Traditional approaches to estimating the penetration depth of a projectile into a granular medium include force laws and scaling relationships formulated from laboratory experiments in terrestrial-gravity conditions. However, it is unclear that these formulations extend to the OSIRIS-REx scenario of a 1300-kg spacecraft interacting with regolith in a microgravity environment. We studied the TAGSAM interaction with Bennu through numerical simulations using two collisional codes, pkdgrav and gdc-i. We validated their accuracy by reproducing the results of laboratory impact experiments in terrestrial gravity. We then performed TAGSAM penetration simulations varying the following geotechnical properties of the regolith: packing fraction (P), bulk density, inter-particle cohesion (σc), and angle of friction (φ). We find that the outcome of a spacecraft-regolith impact has a non-linear dependence on packing fraction. Closely packed regolith (P ? 0.6) can effectively resist the penetration of TAGSAM if φ ? 28° and/or σc ? 50 Pa. For loosely packed regolith (P ? 0.5), the penetration depth is governed by a drag force that scales with impact velocity to the 4/3 power, consistent with energy conservation. We discuss the importance of low-speed impact studies for predicting and interpreting spacecraft-surface interactions. We show that these low-energy events also provide a framework for interpreting the burial depths of large boulders in asteroidal regolith.

@en

The growing increase of renewable generation worldwide is posing new challenges for a secure, reliable and economic operation of power systems. In order to face the uncertain and intermittent production of renewable sources, operating reserves must be allocated efficiently and accurately. Nowadays, these reserves are mainly assigned to thermal units, especially gas-fired generators, due to their operation flexibility and fast response. However, the ramping capabilities of these units define the grade of flexibility offered to the system operation. In practical applications, ramping limits are dynamic, i.e., they are a function of the unit's generating output. Omitting this feature leads to suboptimal or even infeasible reserve allocations, thus increasing not only operating reserve requirements but also transactions in real-time balancing markets needed to back up deviations of renewable generation. This paper contributes with a mixed-integer linear programming model for units' dynamic ramping allowing intraperiod changes in the unit commitment problem. As a result, operating reserves are better allocated and the units' flexibility is managed more efficiently than traditional ramping models found in the literature. Different case studies illustrate the functioning and benefits of the proposed formulation.@en

This paper details a decision model to implement direct load control (DLC) on battery charging processes at electric vehicle charging points located at parking areas. The programming model combines optimally three types of energy management decisions: grid-to-vehicle charges, vehicle-to-grid discharges, and novel vehicle-to-vehicle energy exchanges. The objective function maximizes the net energy supplied to batteries minimizing simultaneously the global energy cost. A 50-plug-in vehicle park case is analyzed for three possible mobility patterns: household, commercial and mixed. Outputs from the DLC model are compared with the ones using a dumb charging policy from the service quality and economic points of view. Finally, a sensitivity analysis has been done to evaluate the economic impact of the Depth of Discharge condition to preserve battery lifecycle of electric vehicles.

@en