A Scenario-Based Branch-and-Bound Approach for MES Scheduling in Urban Buildings

Dan, M and Srinivasan, S and Sundaram, S and Easwaran, A and Glielmo, L (2020) A Scenario-Based Branch-and-Bound Approach for MES Scheduling in Urban Buildings. In: IEEE Transactions on Industrial Informatics, 16 (12). pp. 7510-7520.

PDF ieee_tra_ind_inf_16_12_7510-7520.pdf - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

This article presents a novel solution technique for scheduling multi-energy system (MES) in a commercial urban building to perform price-based demand response and reduce energy costs. The MES scheduling problem is formulated as a mixed integer nonlinear program (MINLP), a nonconvex NP-hard problem with uncertainties due to renewable generation and demand. A model predictive control approach is used to handle the uncertainties and price variations. This in-turn requires solving a time-coupled multitime step MINLP during each time-epoch, which is computationally intensive. This investigation proposes an approach called the scenario-based branch-and-bound (SB3), a light-weight solver to reduce the computational complexity. It combines the simplicity of convex programs with the ability of meta-heuristic techniques to handle complex nonlinear problems. The performance of the SB3 solver is validated in the Cleantech building, Singapore and the results demonstrate that the proposed algorithm reduces energy cost by about 17.26 and 22.46 as against solving a multi-time step heuristic optimization model. © 2005-2012 IEEE.

Item Type:	Journal Article
Publication:	IEEE Transactions on Industrial Informatics
Publisher:	IEEE Computer Society
Additional Information:	copyright for this article belongs to IEEE Computer Society
Keywords:	Branch and bound method; Convex optimization; Heuristic methods; Integer programming; Model predictive control; Nonlinear programming; NP-hard; Scheduling, Branch-and-bound approaches; Heuristic optimization; Meta-heuristic techniques; Mixed integer nonlinear program; Model-predictive control approach; Multi-energy systems; Renewable generation; Solution techniques, Cost reduction
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	18 Nov 2020 06:38
Last Modified:	18 Nov 2020 06:38
URI:	http://eprints.iisc.ac.in/id/eprint/66960

Actions (login required)

View Item