68,760 research outputs found
Cover Tree Bayesian Reinforcement Learning
This paper proposes an online tree-based Bayesian approach for reinforcement
learning. For inference, we employ a generalised context tree model. This
defines a distribution on multivariate Gaussian piecewise-linear models, which
can be updated in closed form. The tree structure itself is constructed using
the cover tree method, which remains efficient in high dimensional spaces. We
combine the model with Thompson sampling and approximate dynamic programming to
obtain effective exploration policies in unknown environments. The flexibility
and computational simplicity of the model render it suitable for many
reinforcement learning problems in continuous state spaces. We demonstrate this
in an experimental comparison with least squares policy iteration
Optimal Net-Load Balancing in Smart Grids with High PV Penetration
Mitigating Supply-Demand mismatch is critical for smooth power grid
operation. Traditionally, load curtailment techniques such as Demand Response
(DR) have been used for this purpose. However, these cannot be the only
component of a net-load balancing framework for Smart Grids with high PV
penetration. These grids can sometimes exhibit supply surplus causing
over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are
complex and computationally expensive. This increases the complexity of
net-load balancing systems used by the grid operator and limits their
scalability. Recently new technologies have been developed that enable the
rapid and selective connection of PV modules of an installation to the grid.
Taking advantage of these advancements, we develop a unified optimal net-load
balancing framework which performs both load and solar curtailment. We show
that when the available curtailment values are discrete, this problem is
NP-hard and develop bounded approximation algorithms for minimizing the
curtailment cost. Our algorithms produce fast solutions, given the tight timing
constraints required for grid operation. We also incorporate the notion of
fairness to ensure that curtailment is evenly distributed among all the nodes.
Finally, we develop an online algorithm which performs net-load balancing using
only data available for the current interval. Using both theoretical analysis
and practical evaluations, we show that our net-load balancing algorithms
provide solutions which are close to optimal in a small amount of time.Comment: 11 pages. To be published in the 4th ACM International Conference on
Systems for Energy-Efficient Built Environments (BuildSys 17) Changes from
previous version: Fixed a bug in Algorithm 1 which was causing some min cost
solutions to be misse
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