Stochastic framework for peak demand reduction opportunities with solar energy for manufacturing facilities

• Published in: Journal of cleaner production Vol. 313; no. C; p. 127891
• Main Authors: Peinado-Guerrero, Miguel A., Villalobos, Jesus R., Phelan, Patrick E., Campbell, Nicolas A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2021; Elsevier
• Subjects: Demand response; Demand-side management; Engineering; Environmental Sciences & Ecology; Markov chains; Renewable integration; Science & Technology - Other Topics; Stochastic; Renewable integration; Markov chains; Demand response; Stochastic; Demand-side management
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2021.127891

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%. •Not considering variability may lead to suboptimal demand-side strategies.•Considering stochasticity can result in cost reductions for the users and utilities.•Modeling is used for distributed energy resources peak demand reduction.•An SPV installation is used to highlight the benefits of the models presented.