Bhandare_Onkar.pdf (1.28 MB)
Simulation Based Electricity Demand Response Considering Product Sequence and Onsite Renewable Energy
thesisposted on 2016-10-18, 00:00 authored by Onkar A. Bhandare
The growing electricity demand during peak demand periods has resulted in the need to build and develop additional infrastructure. Moreover, the rise in fuel prices have opened up opportunities to investigate renewable energy technologies (RET) to meet this growing demand. Greenhouse gas (GHG) emissions can be reduced and significant savings can be achieved on electricity bills and carbon credits. Compared to the existing literature on energy load management, very few studies integrating electricity demand response programs and renewable energy have been conducted. In this thesis, initially, we develop a discrete event simulation (DES) model considering a manufacturing facility with multiple stations and multiple product types. The production schedule is determined by optimizing the product sequence and labor requirement under the constraint of production throughput using simulation based optimization (SBO). Later, we establish an agent based simulation model considering a renewable Distributed Generation (DG) system and a Time of use (TOU) electricity pricing program to minimize the electricity cost incurred from the grid. The DG system features on-site generation from RET and a battery storage. The dispatch strategy of the DG system is based on the on-peak and off-peak periods of the day, availability of renewable energy, energy demand, and battery state of charge. Also, the size of battery storage is determined using SBO for specified capacities of RET. The capacities of the DG system are chosen from the annual savings plot. Two case studies considering a PV-battery hybrid system and a PV-wind-battery hybrid system are compared with the baseline scenario to illustrate the effectiveness of the proposed model.
DepartmentMechanical and Industrial Engineering
Degree GrantorUniversity of Illinois at Chicago
Committee MemberHu, Mengqi Scott, Michael