A Multi–Parametric Optimization Strategy for Multilevel Hierarchical Control problems

Faísca, Nuno P.; Kouramas, Konstantinos; Pistikopoulos, Efstratios N.

doi:10.3182/20080706-5-kr-1001.01064

Cited by 1 publication

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[5], for instance, develops an enhanced integrated model, which includes automatic acquisition of parameters. [7] considers a general threelevel hierarchical control problem and attempts to solve it by recasting the optimisation problem at each level as a multiparametric programming problem. [8] considers hierarchical control of electricity consumers in a Smart Grid setting and The authors are with the Department of Electronic Systems, Automation and Control, Aalborg University, Denmark; email: {dimon, ktr}@es.aau.dk introduces artificial aggregators as a way of dealing with computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Exact constraint aggregation with applications to smart grids and resource distribution

Trangbaek

Bendtsen

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

View full text Add to dashboard Cite

As hierarchical predictive control of large-scale distributed systems grow in complexity, it eventually becomes necessary to consider aggregation of lower-level units into larger groups of units that can be handled efficiently at higher levels in the hierarchy. When aggregating similar units in this manner, it is advantageous if the aggregation maintains a certain degree of genericity, since the higher-level algorithms can then be designed with a higher degree of modularity. To achieve this goal, however, it is not only necessary to examine aggregation of models of the underlying units, but also the accompanying constraints. Constraint sets for rate-and storage volumeconstrained units can often be represented as polytopes in high-dimensional Euclidean space; unfortunately, adding such polytopic sets in higher dimension than 2 has so far been considered a combinatorial problem. In this paper, we present a novel method for computing such polytopic constraint sets for integrating units, which achieves a much lower computational complexity than previous results. The concept is demonstrated via simulations of a smart grid control scenario.

show abstract

Section: Introductionmentioning

confidence: 99%