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Identification of reservation capacity in critical peak pricing electricity demand response program for sustainable manufacturing systems
Author(s) -
Bego Andres,
Li Lin,
Sun Zeyi
Publication year - 2014
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3077
Subject(s) - demand response , reservation , electricity , scheduling (production processes) , electricity pricing , computer science , integer programming , production (economics) , electricity generation , linear programming , operations research , environmental economics , mathematical optimization , engineering , electricity market , economics , operations management , microeconomics , power (physics) , electrical engineering , computer network , physics , quantum mechanics , mathematics , algorithm
SUMMARY Electricity demand response is considered an effective approach to balance the electricity demand and supply with existing infrastructure of generation, transmission, and distribution. A majority of existing literature on the electricity demand response has mainly centered on the commercial and residential building sectors while the application for the industrial sector is largely neglected. This paper presents a methodology for the application of a typical demand response program, Critical Peak Pricing (CPP) program, for the manufacturing enterprises. The configuration of the reservation capacity (in kilowatt, kW) in the CPP program, which plays a critical role in the cost of the final bill charge, will be identified by optimal production scheduling for the typical manufacturing systems with multiple machines and buffers. Mixed Integer Nonlinear Programming formulation is used to establish the mathematical model with the objective to minimize the electricity bill cost as well as the potential penalty cost due to the non‐fulfillment of the target production. An approximate technique is introduced to find a near optimal solution, and a numerical case study is used to illustrate the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.