9,798 research outputs found
Cooperation and Storage Tradeoffs in Power-Grids with Renewable Energy Resources
One of the most important challenges in smart grid systems is the integration
of renewable energy resources into its design. In this work, two different
techniques to mitigate the time varying and intermittent nature of renewable
energy generation are considered. The first one is the use of storage, which
smooths out the fluctuations in the renewable energy generation across time.
The second technique is the concept of distributed generation combined with
cooperation by exchanging energy among the distributed sources. This technique
averages out the variation in energy production across space. This paper
analyzes the trade-off between these two techniques. The problem is formulated
as a stochastic optimization problem with the objective of minimizing the time
average cost of energy exchange within the grid. First, an analytical model of
the optimal cost is provided by investigating the steady state of the system
for some specific scenarios. Then, an algorithm to solve the cost minimization
problem using the technique of Lyapunov optimization is developed and results
for the performance of the algorithm are provided. These results show that in
the presence of limited storage devices, the grid can benefit greatly from
cooperation, whereas in the presence of large storage capacity, cooperation
does not yield much benefit. Further, it is observed that most of the gains
from cooperation can be obtained by exchanging energy only among a few energy
harvesting sources
A duality-based approach for distributed min-max optimization with application to demand side management
In this paper we consider a distributed optimization scenario in which a set
of processors aims at minimizing the maximum of a collection of "separable
convex functions" subject to local constraints. This set-up is motivated by
peak-demand minimization problems in smart grids. Here, the goal is to minimize
the peak value over a finite horizon with: (i) the demand at each time instant
being the sum of contributions from different devices, and (ii) the local
states at different time instants being coupled through local dynamics. The
min-max structure and the double coupling (through the devices and over the
time horizon) makes this problem challenging in a distributed set-up (e.g.,
well-known distributed dual decomposition approaches cannot be applied). We
propose a distributed algorithm based on the combination of duality methods and
properties from min-max optimization. Specifically, we derive a series of
equivalent problems by introducing ad-hoc slack variables and by going back and
forth from primal and dual formulations. On the resulting problem we apply a
dual subgradient method, which turns out to be a distributed algorithm. We
prove the correctness of the proposed algorithm and show its effectiveness via
numerical computations.Comment: arXiv admin note: substantial text overlap with arXiv:1611.0916
Simplified Algorithm for Dynamic Demand Response in Smart Homes Under Smart Grid Environment
Under Smart Grid environment, the consumers may respond to incentive--based
smart energy tariffs for a particular consumption pattern. Demand Response (DR)
is a portfolio of signaling schemes from the utility to the consumers for load
shifting/shedding with a given deadline. The signaling schemes include
Time--of--Use (ToU) pricing, Maximum Demand Limit (MDL) signals etc. This paper
proposes a DR algorithm which schedules the operation of home appliances/loads
through a minimization problem. The category of loads and their operational
timings in a day have been considered as the operational parameters of the
system. These operational parameters determine the dynamic priority of a load,
which is an intermediate step of this algorithm. The ToU pricing, MDL signals,
and the dynamic priority of loads are the constraints in this formulated
minimization problem, which yields an optimal schedule of operation for each
participating load within the consumer provided duration. The objective is to
flatten the daily load curve of a smart home by distributing the operation of
its appliances in possible low--price intervals without violating the MDL
constraint. This proposed algorithm is simulated in MATLAB environment against
various test cases. The obtained results are plotted to depict significant
monetary savings and flattened load curves.Comment: This paper was accepted and presented in 2019 IEEE PES GTD Grand
International Conference and Exposition Asia (GTD Asia). Furthermore, the
conference proceedings has been published in IEEE Xplor
Distributed reactive power feedback control for voltage regulation and loss minimization
We consider the problem of exploiting the microgenerators dispersed in the
power distribution network in order to provide distributed reactive power
compensation for power losses minimization and voltage regulation. In the
proposed strategy, microgenerators are smart agents that can measure their
phasorial voltage, share these data with the other agents on a cyber layer, and
adjust the amount of reactive power injected into the grid, according to a
feedback control law that descends from duality-based methods applied to the
optimal reactive power flow problem. Convergence to the configuration of
minimum losses and feasible voltages is proved analytically for both a
synchronous and an asynchronous version of the algorithm, where agents update
their state independently one from the other. Simulations are provided in order
to illustrate the performance and the robustness of the algorithm, and the
innovative feedback nature of such strategy is discussed
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