419 research outputs found
Load curve data cleansing and imputation via sparsity and low rank
The smart grid vision is to build an intelligent power network with an
unprecedented level of situational awareness and controllability over its
services and infrastructure. This paper advocates statistical inference methods
to robustify power monitoring tasks against the outlier effects owing to faulty
readings and malicious attacks, as well as against missing data due to privacy
concerns and communication errors. In this context, a novel load cleansing and
imputation scheme is developed leveraging the low intrinsic-dimensionality of
spatiotemporal load profiles and the sparse nature of "bad data.'' A robust
estimator based on principal components pursuit (PCP) is adopted, which effects
a twofold sparsity-promoting regularization through an -norm of the
outliers, and the nuclear norm of the nominal load profiles. Upon recasting the
non-separable nuclear norm into a form amenable to decentralized optimization,
a distributed (D-) PCP algorithm is developed to carry out the imputation and
cleansing tasks using networked devices comprising the so-termed advanced
metering infrastructure. If D-PCP converges and a qualification inequality is
satisfied, the novel distributed estimator provably attains the performance of
its centralized PCP counterpart, which has access to all networkwide data.
Computer simulations and tests with real load curve data corroborate the
convergence and effectiveness of the novel D-PCP algorithm.Comment: 8 figures, submitted to IEEE Transactions on Smart Grid - Special
issue on "Optimization methods and algorithms applied to smart grid
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision
The recent deployment of distributed battery units in prosumer premises offer
new opportunities for providing aggregated flexibility services to both
distribution system operators and balance responsible parties. The optimization
problem presented in this paper is formulated with an objective of cost
minimization which includes energy and battery degradation cost to provide
flexibility services. A decomposed solution approach with the alternating
direction method of multipliers (ADMM) is used instead of commonly adopted
centralised optimization to reduce the computational burden and time, and then
reduce scalability limitations. In this work we apply a modified version of
ADMM that includes two new features with respect to the original algorithm:
first, the primal variables are updated concurrently, which reduces
significantly the computational cost when we have a large number of involved
prosumers; second, it includes a regularization term named Proximal Jacobian
(PJ) that ensures the stability of the solution. A case study is presented for
optimal battery operation of 100 prosumer sites with real-life data. The
proposed method finds a solution which is equivalent to the centralised
optimization problem and is computed between 5 and 12 times faster. Thus,
aggregators or large-scale energy communities can use this scalable algorithm
to provide flexibility services.Comment: 10 pages, 7 figure
- …