Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany. (15th November 2022) - Huang, Pei; Han, Mengjie; Zhang, Xingxing; Hussain, Syed Asad; Jayprakash Bhagat, Rohit; Hogarehalli Kumar, Deepu
Highlights: Propose two parameters to quantify energy sharing performances in a building community. Investigate various factors' impacts on energy sharing performances in three countries using real data. Verify effectiveness of the two proposed parameters in quantifying energy sharing performances. Develop a design optimization method of influential factors to maximize energy sharing potentials. Significantly increase the renewable self-utilization and reduce electricity bills and grid interactions. Abstract: Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors' impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors' impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity . A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future.