13,540 research outputs found
Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach
This paper investigates the fundamental coupling between loads and locational
marginal prices (LMPs) in security-constrained economic dispatch (SCED).
Theoretical analysis based on multi-parametric programming theory points out
the unique one-to-one mapping between load and LMP vectors. Such one-to-one
mapping is depicted by the concept of system pattern region (SPR) and
identifying SPRs is the key to understanding the LMP-load coupling. Built upon
the characteristics of SPRs, the SPR identification problem is modeled as a
classification problem from a market participant's viewpoint, and a Support
Vector Machine based data-driven approach is proposed. It is shown that even
without the knowledge of system topology and parameters, the SPRs can be
estimated by learning from historical load and price data. Visualization and
illustration of the proposed data-driven approach are performed on a 3-bus
system as well as the IEEE 118-bus system
Polyhedral Predictive Regions For Power System Applications
Despite substantial improvement in the development of forecasting approaches,
conditional and dynamic uncertainty estimates ought to be accommodated in
decision-making in power system operation and market, in order to yield either
cost-optimal decisions in expectation, or decision with probabilistic
guarantees. The representation of uncertainty serves as an interface between
forecasting and decision-making problems, with different approaches handling
various objects and their parameterization as input. Following substantial
developments based on scenario-based stochastic methods, robust and
chance-constrained optimization approaches have gained increasing attention.
These often rely on polyhedra as a representation of the convex envelope of
uncertainty. In the work, we aim to bridge the gap between the probabilistic
forecasting literature and such optimization approaches by generating forecasts
in the form of polyhedra with probabilistic guarantees. For that, we see
polyhedra as parameterized objects under alternative definitions (under
and norms), the parameters of which may be modelled and predicted.
We additionally discuss assessing the predictive skill of such multivariate
probabilistic forecasts. An application and related empirical investigation
results allow us to verify probabilistic calibration and predictive skills of
our polyhedra.Comment: 8 page
Learning Price-Elasticity of Smart Consumers in Power Distribution Systems
Demand Response is an emerging technology which will transform the power grid
of tomorrow. It is revolutionary, not only because it will enable peak load
shaving and will add resources to manage large distribution systems, but mainly
because it will tap into an almost unexplored and extremely powerful pool of
resources comprised of many small individual consumers on distribution grids.
However, to utilize these resources effectively, the methods used to engage
these resources must yield accurate and reliable control. A diversity of
methods have been proposed to engage these new resources. As opposed to direct
load control, many methods rely on consumers and/or loads responding to
exogenous signals, typically in the form of energy pricing, originating from
the utility or system operator. Here, we propose an open loop
communication-lite method for estimating the price elasticity of many customers
comprising a distribution system. We utilize a sparse linear regression method
that relies on operator-controlled, inhomogeneous minor price variations, which
will be fair to all the consumers. Our numerical experiments show that reliable
estimation of individual and thus aggregated instantaneous elasticities is
possible. We describe the limits of the reliable reconstruction as functions of
the three key parameters of the system: (i) ratio of the number of
communication slots (time units) per number of engaged consumers; (ii) level of
sparsity (in consumer response); and (iii) signal-to-noise ratio.Comment: 6 pages, 5 figures, IEEE SmartGridComm 201
- …