A multi-dimension clustering-based method for renewable energy investment planning. (July 2021)

• Record Type: Journal Article
• Authors: Liu, Aaron; Miller, Wendy; Cholette, Michael E.; Ledwich, Gerard; Crompton, Glenn; Li, Yong
• Is Part Of: Renewable energy. Volume 172(2021)
• Page Start: 651
• Page End: 666
• Publication Date: 2021-07
• Subjects: Data analysis -- Gaussian mixture model clustering -- Investment strategy -- Performance based planning -- Renewable integration -- Sustainable development; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.03.056
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: As electricity prices and environmental awareness increase, more customers are becoming interested in installing distributed renewable generation, such as rooftop photovoltaic systems. Yearly load profile data could become very relevant to these customers to help them to time efficiently and accurately determine optimal energy investments for these customers. A new multi-dimension objective-oriented clustering-based method (MOC) is developed to identify a set of typical energy and/or demand periods. These typical periods can then be used to quantify the yearly cost savings for various renewable energy investment options. The optimal investment option can be determined after examining the financial viability of each option. This method was applied to a real community case study to evaluate renewable energy generation and storage options under two tariff situations: energy only or peak demand. Simulation results show that the MOC method can guide renewable energy investment planning with significant computational time reduction and high accuracy, compared to iterative simulations using a year of electricity load data. This energy investment planning method can help enable informed distributed renewable energy investment practices. Highlights: A clustering-based method to assess sustainable energy investment options. A real site with combinations of PVs, batteries, different controls and tariffs. Achieved 95% time saving and 97% or higher accuracy for the site case study.