646 research outputs found
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts
We develop a stochastic equilibrium model for an electricity market with
asymmetric renewable energy forecasts. In our setting, market participants
optimize their profits using public information about a conditional expectation
of energy production but use private information about the forecast error
distribution. This information is given in the form of samples and incorporated
into profit-maximizing optimizations of market participants through chance
constraints. We model information asymmetry by varying the sample size of
participants' private information. We show that with more information
available, the equilibrium gradually converges to the ideal solution provided
by the perfect information scenario. Under information scarcity, however, we
show that the market converges to the ideal equilibrium if participants are to
infer the forecast error distribution from the statistical properties of the
data at hand or share their private forecasts
Convex Relaxations and Approximations of Chance-Constrained AC-OPF Problems
This paper deals with the impact of linear approximations for the unknown
nonconvex confidence region of chance-constrained AC optimal power flow
problems. Such approximations are required for the formulation of tractable
chance constraints. In this context, we introduce the first formulation of a
chance-constrained second-order cone (SOC) OPF. The proposed formulation
provides convergence guarantees due to its convexity, while it demonstrates
high computational efficiency. Combined with an AC feasibility recovery, it is
able to identify better solutions than chance-constrained nonconvex AC-OPF
formulations. To the best of our knowledge, this paper is the first to perform
a rigorous analysis of the AC feasibility recovery procedures for robust
SOC-OPF problems. We identify the issues that arise from the linear
approximations, and by using a reformulation of the quadratic chance
constraints, we introduce new parameters able to reshape the approximation of
the confidence region. We demonstrate our method on the IEEE 118-bus system
Electricity Market Equilibrium under Information Asymmetry
We study a competitive electricity market equilibrium with two trading
stages, day-ahead and real-time. The welfare of each market agent is exposed to
uncertainty (here from renewable energy production), while agent information on
the probability distribution of this uncertainty is not identical at the
day-ahead stage. We show a high sensitivity of the equilibrium solution to the
level of information asymmetry and demonstrate economic, operational, and
computational value for the system stemming from potential information sharing
Wind Energy: Forecasting Challenges for its Operational Management
Renewable energy sources, especially wind energy, are to play a larger role
in providing electricity to industrial and domestic consumers. This is already
the case today for a number of European countries, closely followed by the US
and high growth countries, for example, Brazil, India and China. There exist a
number of technological, environmental and political challenges linked to
supplementing existing electricity generation capacities with wind energy.
Here, mathematicians and statisticians could make a substantial contribution at
the interface of meteorology and decision-making, in connection with the
generation of forecasts tailored to the various operational decision problems
involved. Indeed, while wind energy may be seen as an environmentally friendly
source of energy, full benefits from its usage can only be obtained if one is
able to accommodate its variability and limited predictability. Based on a
short presentation of its physical basics, the importance of considering wind
power generation as a stochastic process is motivated. After describing
representative operational decision-making problems for both market
participants and system operators, it is underlined that forecasts should be
issued in a probabilistic framework. Even though, eventually, the forecaster
may only communicate single-valued predictions. The existing approaches to wind
power forecasting are subsequently described, with focus on single-valued
predictions, predictive marginal densities and space-time trajectories.
Upcoming challenges related to generating improved and new types of forecasts,
as well as their verification and value to forecast users, are finally
discussed.Comment: Published in at http://dx.doi.org/10.1214/13-STS445 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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