9,853 research outputs found
Expectation Optimization with Probabilistic Guarantees in POMDPs with Discounted-sum Objectives
Partially-observable Markov decision processes (POMDPs) with discounted-sum
payoff are a standard framework to model a wide range of problems related to
decision making under uncertainty. Traditionally, the goal has been to obtain
policies that optimize the expectation of the discounted-sum payoff. A key
drawback of the expectation measure is that even low probability events with
extreme payoff can significantly affect the expectation, and thus the obtained
policies are not necessarily risk-averse. An alternate approach is to optimize
the probability that the payoff is above a certain threshold, which allows
obtaining risk-averse policies, but ignores optimization of the expectation. We
consider the expectation optimization with probabilistic guarantee (EOPG)
problem, where the goal is to optimize the expectation ensuring that the payoff
is above a given threshold with at least a specified probability. We present
several results on the EOPG problem, including the first algorithm to solve it.Comment: Full version of a paper published at IJCAI/ECAI 201
Optimizing Energy Storage Participation in Emerging Power Markets
The growing amount of intermittent renewables in power generation creates
challenges for real-time matching of supply and demand in the power grid.
Emerging ancillary power markets provide new incentives to consumers (e.g.,
electrical vehicles, data centers, and others) to perform demand response to
help stabilize the electricity grid. A promising class of potential demand
response providers includes energy storage systems (ESSs). This paper evaluates
the benefits of using various types of novel ESS technologies for a variety of
emerging smart grid demand response programs, such as regulation services
reserves (RSRs), contingency reserves, and peak shaving. We model, formulate
and solve optimization problems to maximize the net profit of ESSs in providing
each demand response. Our solution selects the optimal power and energy
capacities of the ESS, determines the optimal reserve value to provide as well
as the ESS real-time operational policy for program participation. Our results
highlight that applying ultra-capacitors and flywheels in RSR has the potential
to be up to 30 times more profitable than using common battery technologies
such as LI and LA batteries for peak shaving.Comment: The full (longer and extended) version of the paper accepted in IGSC
201
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