59,671 research outputs found
A Novel Dynamic Appliance Clustering Scheme in a Community Home Energy Management System for Improved Stability and Resiliency of Microgrids
Power scheduling of domestic appliances is a vital preference for bridging the gap between demand and generation of electricity in a microgrid. For a stable microgrid, an acceptable mechanism must reduce the peak to average ratio (PAR) of power demand with supplementary benefits for consumers as reduced electricity charges. Recent studies have focused on PAR and cost reduction for a small consumer population. Furthermore, researchers have mainly considered homogeneous consumer loads. This study focuses on residential power scheduling for electricity cost reduction for consumers and load profile PAR curtailment for a relatively large consumer population with non-homogeneous loads. A sample population of 1000 consumers from various classes of society is considered. The proposed dynamic clustered community home energy management system (DCCHEMS) allows the clustering of appliances based on time overlap criteria. Comparatively flatter power demand is attained by utilizing the clustered appliances in conjunction with particle swarm optimization under the influence of user-defined constraints. Modified inclined block rates with real-time electricity pricing strategies are deployed to minimize the electricity costs. DCCHEMS achieved higher efficiency rates in contrast to the traditional non-clustering and static clustering optimization schemes. An improvement of 21% in peak to average ratio, 4% in cost reduction, and 19% in variance to mean ratio is obtained
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads via
platooning, even when human drivers and autonomous vehicles share roads.
However, when users of a road network choose their routes selfishly, the
resulting traffic configuration may be very inefficient. Because of this, we
consider how to influence human decisions so as to decrease congestion on these
roads. We consider a network of parallel roads with two modes of
transportation: (i) human drivers who will choose the quickest route available
to them, and (ii) ride hailing service which provides an array of autonomous
vehicle ride options, each with different prices, to users. In this work, we
seek to design these prices so that when autonomous service users choose from
these options and human drivers selfishly choose their resulting routes, road
usage is maximized and transit delay is minimized. To do so, we formalize a
model of how autonomous service users make choices between routes with
different price/delay values. Developing a preference-based algorithm to learn
the preferences of the users, and using a vehicle flow model related to the
Fundamental Diagram of Traffic, we formulate a planning optimization to
maximize a social objective and demonstrate the benefit of the proposed routing
and learning scheme.Comment: Submitted to CDC 201
Scale-Adaptive Group Optimization for Social Activity Planning
Studies have shown that each person is more inclined to enjoy a group
activity when 1) she is interested in the activity, and 2) many friends with
the same interest join it as well. Nevertheless, even with the interest and
social tightness information available in online social networks, nowadays many
social group activities still need to be coordinated manually. In this paper,
therefore, we first formulate a new problem, named Participant Selection for
Group Activity (PSGA), to decide the group size and select proper participants
so that the sum of personal interests and social tightness of the participants
in the group is maximized, while the activity cost is also carefully examined.
To solve the problem, we design a new randomized algorithm, named Budget-Aware
Randomized Group Selection (BARGS), to optimally allocate the computation
budgets for effective selection of the group size and participants, and we
prove that BARGS can acquire the solution with a guaranteed performance bound.
The proposed algorithm was implemented in Facebook, and experimental results
demonstrate that social groups generated by the proposed algorithm
significantly outperform the baseline solutions.Comment: 20 pages. arXiv admin note: substantial text overlap with
arXiv:1305.150
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