Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach - Hafiz, Faeza; Rodrigo de Queiroz, Anderson; Fajri, Poria; Husain, Iqbal (Applied energy, 2019)
•We propose a cost-effective energy management algorithm for PV-storage in the context of a shared community.•Uncertainties related with electricity demand and solar power generation are used in the decision-making process.•The impact of energy management is included for net present value (NPV) calculation of each system design.•An approach to identify the optimal storage sizing using NPV is presented and applied to each house of the community. The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization. To evaluate that, a multi-stage stochastic program model designed to minimize community electricity purchase cost per day is used to support decision-making by creating control policies for energy management. Two different strategies are created to represent the interest of a single household (the individual energy management - IEM) and households that share their assets with the community (shared energy management - SEM). Our strategies consider time-of-use rates (ToU), load and resource variation during different seasons, with their distinct days of the year, to calculate net present value (NPV) associated with the energy savings. IEM and SEM are then used in a framework designed to establish the requirement of optimal energy storage size for each house of the community based on NPV values. The results of this study for an analysis considering a community with five houses show that the proposed SEM strategy reduces the overall electricity purchase costs for a summer day up to 11% and 3% compared with heuristic and IEM control respectively. Moreover, our results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%.