22,730 research outputs found
Learning from past bids to participate strategically in day-ahead electricity markets
We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent pricesFirst author draf
Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets
We consider the process of bidding by electricity suppliers in a day-ahead
market context where each supplier bids a linear non-decreasing function of her
generating capacity with the goal of maximizing her individual profit given
other competing suppliers' bids. Based on the submitted bids, the market
operator schedules suppliers to meet demand during each hour and determines
hourly market clearing prices. Eventually, this game-theoretic process reaches
a Nash equilibrium when no supplier is motivated to modify her bid. However,
solving the individual profit maximization problem requires information of
rivals' bids, which are typically not available. To address this issue, we
develop an inverse optimization approach for estimating rivals' production cost
functions given historical market clearing prices and production levels. We
then use these functions to bid strategically and compute Nash equilibrium
bids. We present numerical experiments illustrating our methodology, showing
good agreement between bids based on the estimated production cost functions
with the bids based on the true cost functions. We discuss an extension of our
approach that takes into account network congestion resulting in
location-dependent prices
Compositional modulation in AlxGa1−xAs epilayers grown by molecular beam epitaxy on the (111) facets of grooves in a nonplanar substrate
We report the first observation of a lateral junction formed in an alloy due to an abrupt transition from segregated to random AlGaAs alloy compositions. Al0.25Ga0.75As epilayers were grown by molecular beam epitaxy on [011-bar] oriented grooves in a nonplanar (100) GaAs substrate. A quasi-periodic modulation of the aluminum concentration occurs spontaneously in material grown on the (111) facets of the groove, with a period of 50–70 Å along the [111] direction. The compositional modulation is associated with a reduction of the band gap by 130 meV, with respect to the random alloy. While segregation of the AlGaAs alloy has been seen previously, this is the first observation of segregation of AlGaAs grown on a (111) surface. The compositional modulation terminates abruptly at the boundaries of the (111) facet, forming abrupt lateral junctions in the AlGaAs layers grown on a groove
Complexity in surfaces of densest packings for families of polyhedra
Packings of hard polyhedra have been studied for centuries due to their
mathematical aesthetic and more recently for their applications in fields such
as nanoscience, granular and colloidal matter, and biology. In all these
fields, particle shape is important for structure and properties, especially
upon crowding. Here, we explore packing as a function of shape. By combining
simulations and analytic calculations, we study three 2-parameter families of
hard polyhedra and report an extensive and systematic analysis of the densest
packings of more than 55,000 convex shapes. The three families have the
symmetries of triangle groups (icosahedral, octahedral, tetrahedral) and
interpolate between various symmetric solids (Platonic, Archimedean, Catalan).
We find that optimal (maximum) packing density surfaces that reveal unexpected
richness and complexity, containing as many as 130 different structures within
a single family. Our results demonstrate the utility of thinking of shape not
as a static property of an object in the context of packings, but rather as but
one point in a higher dimensional shape space whose neighbors in that space may
have identical or markedly different packings. Finally, we present and
interpret our packing results in a consistent and generally applicable way by
proposing a method to distinguish regions of packings and classify types of
transitions between them.Comment: 16 pages, 8 figure
Predictive protocol of flocks with small-world connection pattern
By introducing a predictive mechanism with small-world connections, we
propose a new motion protocol for self-driven flocks. The small-world
connections are implemented by randomly adding long-range interactions from the
leader to a few distant agents, namely pseudo-leaders. The leader can directly
affect the pseudo-leaders, thereby influencing all the other agents through
them efficiently. Moreover, these pseudo-leaders are able to predict the
leader's motion several steps ahead and use this information in decision making
towards coherent flocking with more stable formation. It is shown that drastic
improvement can be achieved in terms of both the consensus performance and the
communication cost. From the industrial engineering point of view, the current
protocol allows for a significant improvement in the cohesion and rigidity of
the formation at a fairly low cost of adding a few long-range links embedded
with predictive capabilities. Significantly, this work uncovers an important
feature of flocks that predictive capability and long-range links can
compensate for the insufficiency of each other. These conclusions are valid for
both the attractive/repulsive swarm model and the Vicsek model.Comment: 10 pages, 12 figure
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