Optimal Sizing and Planning of Onsite Generation System for Manufacturing in Critical Peaking Pricing Demand Response Program

Abstract

Onsite electricity generation system in manufacturing has been traditionally considered an effective backup energy source to support the manufacturing operations when external power is not available due to natural disasters and/or power blackouts. Recently, with the increasing concerns of climate change and environmental protection, the contribution of using onsite generation system (OGS) to the manufacturing end use customers when they enroll in specific electricity demand response programs has also been gradually recognized. In this paper, we investigate the cost-effective OGS sizing problem for manufacturing practitioners when participating in Critical Peaking Pricing (CPP) demand response program. A Mixed Integer Non-Linear Programming (MINLP) formulation is proposed to identify the optimal size and utilization strategy of the OGS, as well as the corresponding production plan of the manufacturing system to minimize the overall energy related cost. Linearization strategy and metaheuristic algorithm are discussed for solving the proposed formulation with a reasonable computational cost and a good solution quality. A case study based on a real auto component manufacturing system and an existing CPP program is implemented to examine the effects of the proposed model. The results show that when utilizing the OGS appropriately sized, the total electricity related cost of the manufacturing system can be significantly reduced when participating in the CPP program.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Critical peak pricing; Demand response; Genetic algorithm; Manufacturing system; Onsite generation system; Sizing

International Standard Serial Number (ISSN)

0925-5273

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Elsevier, All rights reserved.

Publication Date

01 Dec 2018

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