A multi-dimension clustering-based method for renewable energy investment planning

RENEWABLE ENERGY

卷:172

Article

Liu, Aaron¹  Miller, Wendy¹  Cholette, Michael E.¹  Ledwich, Gerard¹  Crompton, Glenn¹  Li, Yong² 

[1] Queensland Univ Technol, Fac Engn, 2 George St, Brisbane, Qld 4000, Australia

[2] Hunan Univ, Coll Elect & Informat Engn, Lushan Nan Rd, Changsha 410082, Hunan, Peoples R China

关键词: Data analysis;    Gaussian mixture model clustering;    Investment strategy;    Performance based planning;    Renewable integration;    Sustainable development;   

DOI  :  10.1016/j.renene.2021.03.056

来源: Elsevier

【 摘 要 】

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. (c) 2021 Elsevier Ltd. All rights reserved.

【 授权许可】

Free