Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/101192
Title: Modelling state spaces and discrete control using MILP: computational cost considerations for demand response
Authors: Magalhães, P. L. 
Antunes, C. H. 
Keywords: computational performance; state space; discrete control; mixed-integer linear programming; multiple-choice programming
Issue Date: 2021
Project: FCT - UIDB/00308/2020 
project ESGRIDS (POCI-01-0145-FEDER-016434), 
project MAnAGER (POCI-01-0145-FEDER-028040) 
project SUSpENsE (CENTRO-01-0145-FEDER-000006). 
Serial title, monograph or event: EAI Endorsed Transactions on Energy Web
Volume: 8
Issue: 34
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, we compare 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.
URI: http://hdl.handle.net/10316/101192
ISSN: 2032-944X
DOI: 10.4108/eai.23-12-2020.167787
Rights: openAccess
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais

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