TY - JOUR
T1 - Stochastic framework for peak demand reduction opportunities with solar energy for manufacturing facilities
AU - Peinado-Guerrero, Miguel A.
AU - Villalobos, Jesus R.
AU - Phelan, Patrick E.
AU - Campbell, Nicolas A.
N1 - Funding Information:
This work was supported by the United States Department of Energy's Industrial Assessment Program [Award Number DE-EE0007721 ]; and the Salt River Project [Award Number 98–153C , Mod-44 Non-EE-10].
Publisher Copyright:
© 2021
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Demand-side management has gained traction as a means for energy service providers to persuade their customers to change the pattern of their energy use. The aim is typically to create a balance between electrical supply and demand, particularly with the introduction of variable distributed resources such as solar. This paper proposes a non-intrusive methodology for evaluating an energy customer's potential for participation in demand-side management programs with a focus on manufacturing facilities. The methodology rests on modeling the stochasticity of individuals' energy loads to estimate when peak demand is most likely to occur. The proposed methodology is applied to the design of solar photovoltaic power generation for the purpose of maximum demand-side peak reduction in the targeted facility. The results of a case study reveal that the proposed methodology enabled a user to attain 2.5% more cost savings, while reducing the amount of electrical energy sold back to the utility company by 45.8%.
AB - Demand-side management has gained traction as a means for energy service providers to persuade their customers to change the pattern of their energy use. The aim is typically to create a balance between electrical supply and demand, particularly with the introduction of variable distributed resources such as solar. This paper proposes a non-intrusive methodology for evaluating an energy customer's potential for participation in demand-side management programs with a focus on manufacturing facilities. The methodology rests on modeling the stochasticity of individuals' energy loads to estimate when peak demand is most likely to occur. The proposed methodology is applied to the design of solar photovoltaic power generation for the purpose of maximum demand-side peak reduction in the targeted facility. The results of a case study reveal that the proposed methodology enabled a user to attain 2.5% more cost savings, while reducing the amount of electrical energy sold back to the utility company by 45.8%.
KW - Demand response
KW - Demand-side management
KW - Markov chains
KW - Renewable integration
KW - Stochastic
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U2 - 10.1016/j.jclepro.2021.127891
DO - 10.1016/j.jclepro.2021.127891
M3 - Article
AN - SCOPUS:85107779221
SN - 0959-6526
VL - 313
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 127891
ER -