1,491 research outputs found
Achieving an optimal trade-off between revenue and energy peak within a smart grid environment
We consider an energy provider whose goal is to simultaneously set
revenue-maximizing prices and meet a peak load constraint. In our bilevel
setting, the provider acts as a leader (upper level) that takes into account a
smart grid (lower level) that minimizes the sum of users' disutilities. The
latter bases its decisions on the hourly prices set by the leader, as well as
the schedule preferences set by the users for each task. Considering both the
monopolistic and competitive situations, we illustrate numerically the validity
of the approach, which achieves an 'optimal' trade-off between three
objectives: revenue, user cost, and peak demand
Time and Location Aware Mobile Data Pricing
Mobile users' correlated mobility and data consumption patterns often lead to
severe cellular network congestion in peak hours and hot spots. This paper
presents an optimal design of time and location aware mobile data pricing,
which incentivizes users to smooth traffic and reduce network congestion. We
derive the optimal pricing scheme through analyzing a two-stage decision
process, where the operator determines the time and location aware prices by
minimizing his total cost in Stage I, and each mobile user schedules his mobile
traffic by maximizing his payoff (i.e., utility minus payment) in Stage II. We
formulate the two-stage decision problem as a bilevel optimization problem, and
propose a derivative-free algorithm to solve the problem for any increasing
concave user utility functions. We further develop low complexity algorithms
for the commonly used logarithmic and linear utility functions. The optimal
pricing scheme ensures a win-win situation for the operator and users.
Simulations show that the operator can reduce the cost by up to 97.52% in the
logarithmic utility case and 98.70% in the linear utility case, and users can
increase their payoff by up to 79.69% and 106.10% for the two types of
utilities, respectively, comparing with a time and location independent pricing
benchmark. Our study suggests that the operator should provide price discounts
at less crowded time slots and locations, and the discounts need to be
significant when the operator's cost of provisioning excessive traffic is high
or users' willingness to delay traffic is low.Comment: This manuscript serves as the online technical report of the article
accepted by IEEE Transactions on Mobile Computin
Integer Bilevel Linear Programming Problems: New Results and Applications
Integer Bilevel Linear Programming Problems: New Results and Application
Integer Bilevel Linear Programming Problems: New Results and Applications
Integer Bilevel Linear Programming Problems: New Results and Application
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