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|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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