1,723 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
A Consensus-ADMM Approach for Strategic Generation Investment in Electricity Markets
This paper addresses a multi-stage generation investment problem for a
strategic (price-maker) power producer in electricity markets. This problem is
exposed to different sources of uncertainty, including short-term operational
(e.g., rivals' offering strategies) and long-term macro (e.g., demand growth)
uncertainties. This problem is formulated as a stochastic bilevel optimization
problem, which eventually recasts as a large-scale stochastic mixed-integer
linear programming (MILP) problem with limited computational tractability. To
cope with computational issues, we propose a consensus version of alternating
direction method of multipliers (ADMM), which decomposes the original problem
by both short- and long-term scenarios. Although the convergence of ADMM to the
global solution cannot be generally guaranteed for MILP problems, we introduce
two bounds on the optimal solution, allowing for the evaluation of the solution
quality over iterations. Our numerical findings show that there is a trade-off
between computational time and solution quality
A Community Microgrid Architecture with an Internal Local Market
This work fits in the context of community microgrids, where members of a
community can exchange energy and services among themselves, without going
through the usual channels of the public electricity grid. We introduce and
analyze a framework to operate a community microgrid, and to share the
resulting revenues and costs among its members. A market-oriented pricing of
energy exchanges within the community is obtained by implementing an internal
local market based on the marginal pricing scheme. The market aims at
maximizing the social welfare of the community, thanks to the more efficient
allocation of resources, the reduction of the peak power to be paid, and the
increased amount of reserve, achieved at an aggregate level. A community
microgrid operator, acting as a benevolent planner, redistributes revenues and
costs among the members, in such a way that the solution achieved by each
member within the community is not worse than the solution it would achieve by
acting individually. In this way, each member is incentivized to participate in
the community on a voluntary basis. The overall framework is formulated in the
form of a bilevel model, where the lower level problem clears the market, while
the upper level problem plays the role of the community microgrid operator.
Numerical results obtained on a real test case implemented in Belgium show
around 54% cost savings on a yearly scale for the community, as compared to the
case when its members act individually.Comment: 16 pages, 15 figure
Opportunities for Price Manipulation by Aggregators in Electricity Markets
Aggregators are playing an increasingly crucial role in the integration of
renewable generation in power systems. However, the intermittent nature of
renewable generation makes market interactions of aggregators difficult to
monitor and regulate, raising concerns about potential market manipulation by
aggregators. In this paper, we study this issue by quantifying the profit an
aggregator can obtain through strategic curtailment of generation in an
electricity market. We show that, while the problem of maximizing the benefit
from curtailment is hard in general, efficient algorithms exist when the
topology of the network is radial (acyclic). Further, we highlight that
significant increases in profit are possible via strategic curtailment in
practical settings
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