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

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 help them time efficiently accurately determine optimal energy investments for customers. A new multi-dimension objective-oriented clustering-based method (MOC) is developed identify a set of typical and/or demand periods. These periods can then be used quantify the yearly cost savings various investment options. The option determined after examining financial viability each option. This was applied real community case study evaluate generation storage options under two tariff situations: only or peak demand. Simulation results show that MOC guide planning with significant computational reduction high accuracy, compared iterative simulations using year data. enable informed practices.