2 research outputs found
Distributed demand-side contingency-service provisioning while minimizing consumer disutility through local frequency measurements and inter-load communication
We consider the problem of smart and flexible loads providing contingency
reserves to the electric grid and provide a Distributed Gradient Projection
(DGP) algorithm to minimize loads' disutility while providing contingency
services. Each load uses locally obtained grid-frequency measurements and
inter-load communication to coordinate their actions, and the privacy of each
load is preserved: only gradient information is exchanged---not disutility or
consumption information. We provide a proof of convergence of the proposed DGP
algorithm, and we compare its performance through simulations to that of a
"dual algorithm" previously proposed in the literature that solved the dual
optimization problem. The DGP algorithm solves the primal problem. Its main
advantage over the dual algorithm is that it is applicable to convex---but not
necessarily strictly convex---consumer disutility functions, such as a model of
consumer behavior that is insensitive to small changes in consumption, while
the dual algorithm is not. Simulations show the DGP algorithm aids in arresting
grid-frequency deviations in response to contingency events and performs better
or similarly to the dual algorithm in cases where the two can be compared
Predictive resource allocation for flexible loads with local QoS
Loads that can vary their power consumption without violating their Quality
of service (QoS), that is flexible loads, are an invaluable resource for grid
operators. Utilizing flexible loads as a resource requires the grid operator to
incorporate them into a resource allocation problem. Since flexible loads are
often consumers, for concerns of privacy it is desirable for this problem to
have a distributed implementation. Technically, this distributed implementation
manifests itself as a time varying convex optimization problem constrained by
the QoS of each load. In the literature, a time invariant form of this problem
without all of the necessary QoS metrics for the flexible loads is often
considered. Moving to a more realistic setup introduces additional technical
challenges, due to the problems' time-varying nature. In this work, we develop
an algorithm to account for the challenges introduced when considering a time
varying setup with appropriate QoS metrics.Comment: 8 pages, 3 figure