Research Article
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response
@ARTICLE{10.4108/eai.23-12-2020.167787, author={P. L. Magalh\"{a}es and C. H. Antunes}, title={Modelling state spaces and discrete control using MILP: computational cost considerations for demand response}, journal={EAI Endorsed Transactions on Energy Web}, volume={8}, number={34}, publisher={EAI}, journal_a={EW}, year={2020}, month={12}, keywords={computational performance, state space, discrete control, mixed-integer linear programming, multiple-choice programming}, doi={10.4108/eai.23-12-2020.167787} }- P. L. Magalhães
C. H. Antunes
Year: 2020
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response
EW
EAI
DOI: 10.4108/eai.23-12-2020.167787
Abstract
INTRODUCTION: Demand response (DR) has been proposed as a mechanism to induce electricity cost reductions and is typically assumed to require the adoption of time-differentiated electricity prices. Making the most of these requires using automated energy management systems to produce optimised DR plans. Mixed-integer linear programming (MILP) has been used for this purpose, including by modelling dynamic systems (DS).
OBJECTIVES: In this paper, wecompare the computational performance of MILP approaches for modelling state spaces and multi-level discrete control (MLDC) in DR problems involving DSs.
METHODS: A state-of-the-art MILP solver was used to compute solutions and compare approaches.
RESULTS: Modelling state spaces using decision variables proved to be the most efficient option in over 80% of cases. In turn, the new MLDC approaches outperformed the standard one in about 60% of cases despite performing in the same range.
CONCLUSION: We conclude that using state variables is generally the better option and that all MLDC variants perform similarly.
Copyright © 2020 P.L. Magalhães et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.