Modelling state spaces and discrete control using MILP: computational cost considerations for demand response

Magalhães, P. L. and Antunes, C. H. (2020) Modelling state spaces and discrete control using MILP: computational cost considerations for demand response. EAI Endorsed Transactions on Energy Web. e4. ISSN 2032-944X

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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.

Item Type: Article
Uncontrolled Keywords: computational performance, state space, discrete control, mixed-integer linear programming, multiple-choice programming
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 22 Jul 2021 13:49
Last Modified: 22 Jul 2021 13:49
URI: https://eprints.eudl.eu/id/eprint/4995

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