6,663 research outputs found
Convex Chance Constrained Model Predictive Control
We consider the Chance Constrained Model Predictive Control problem for
polynomial systems subject to disturbances. In this problem, we aim at finding
optimal control input for given disturbed dynamical system to minimize a given
cost function subject to probabilistic constraints, over a finite horizon. The
control laws provided have a predefined (low) risk of not reaching the desired
target set. Building on the theory of measures and moments, a sequence of
finite semidefinite programmings are provided, whose solution is shown to
converge to the optimal solution of the original problem. Numerical examples
are presented to illustrate the computational performance of the proposed
approach.Comment: This work has been submitted to the 55th IEEE Conference on Decision
and Contro
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Robust Energy Management for Microgrids With High-Penetration Renewables
Due to its reduced communication overhead and robustness to failures,
distributed energy management is of paramount importance in smart grids,
especially in microgrids, which feature distributed generation (DG) and
distributed storage (DS). Distributed economic dispatch for a microgrid with
high renewable energy penetration and demand-side management operating in
grid-connected mode is considered in this paper. To address the intrinsically
stochastic availability of renewable energy sources (RES), a novel power
scheduling approach is introduced. The approach involves the actual renewable
energy as well as the energy traded with the main grid, so that the
supply-demand balance is maintained. The optimal scheduling strategy minimizes
the microgrid net cost, which includes DG and DS costs, utility of dispatchable
loads, and worst-case transaction cost stemming from the uncertainty in RES.
Leveraging the dual decomposition, the optimization problem formulated is
solved in a distributed fashion by the local controllers of DG, DS, and
dispatchable loads. Numerical results are reported to corroborate the
effectiveness of the novel approach.Comment: Short versions were accepted by the IEEE Transactions on Sustainable
Energy, and presented in part at the IEEE SmartGridComm 201
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