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Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program

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  • Zhang, Yunchao
  • Islam, Md Monirul
  • Sun, Zeyi
  • Yang, Sijia
  • Dagli, Cihan
  • Xiong, Haoyi

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.

Suggested Citation

  • Zhang, Yunchao & Islam, Md Monirul & Sun, Zeyi & Yang, Sijia & Dagli, Cihan & Xiong, Haoyi, 2018. "Optimal sizing and planning of onsite generation system for manufacturing in Critical Peaking Pricing demand response program," International Journal of Production Economics, Elsevier, vol. 206(C), pages 261-267.
    • Handle: RePEc:eee:proeco:v:206:y:2018:i:c:p:261-267
    DOI: 10.1016/j.ijpe.2018.10.011
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    References listed on IDEAS

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    1. Doostizadeh, Meysam & Ghasemi, Hassan, 2012. "A day-ahead electricity pricing model based on smart metering and demand-side management," Energy, Elsevier, vol. 46(1), pages 221-230.
    2. Vassileva, Iana & Wallin, Fredrik & Dahlquist, Erik, 2012. "Understanding energy consumption behavior for future demand response strategy development," Energy, Elsevier, vol. 46(1), pages 94-100.
    3. Faria, P. & Vale, Z., 2011. "Demand response in electrical energy supply: An optimal real time pricing approach," Energy, Elsevier, vol. 36(8), pages 5374-5384.
    4. Wang, Yong & Li, Lin, 2016. "Critical peak electricity pricing for sustainable manufacturing: Modeling and case studies," Applied Energy, Elsevier, vol. 175(C), pages 40-53.
    5. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    6. Greening, Lorna A., 2010. "Demand response resources: Who is responsible for implementation in a deregulated market?," Energy, Elsevier, vol. 35(4), pages 1518-1525.
    7. Hawkes, A.D. & Leach, M.A., 2007. "Cost-effective operating strategy for residential micro-combined heat and power," Energy, Elsevier, vol. 32(5), pages 711-723.
    8. Mahdi Hamzeei & James Luedtke, 2014. "Linearization-based algorithms for mixed-integer nonlinear programs with convex continuous relaxation," Journal of Global Optimization, Springer, vol. 59(2), pages 343-365, July.
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