Optimization of an Intelligent Community Hybrid Energy System With Robustness Consideration

• Main Authors: Bin Xu, Yixin Ge, Yufeng Liu, Jin Qi, Yanfei Sun
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Intelligent community hybrid energy optimization; robustness of energy supply; MOEA/D
• Online Access: https://ieeexplore.ieee.org/document/8705319/

An intelligent community hybrid energy system (ICHES) includes renewable energy power generation equipment, distributed controllable generation equipment, energy storage systems, mobile agents, and large numbers of fixed loads. In recent years, many studies of hybrid energy systems have been considered from the perspective of cost. However, the pollution and robustness of the system cannot be ignored. To address these issues, this paper introduces the environmental impact ratio of power generation and power supply robustness into the model and proposes a novel intelligent community hybrid energy optimization model. The proposed model is a multi-objective optimization problem. To address this complex multi-objective optimization problem, a direction vector adjustment (DVA) mechanism is introduced into the multi-objective evolutionary algorithm based on decomposition (MOEA/D) using localized penalty-based boundary intersection (LPBI) (MOEA/D-LPBI). Then, an improved MOEA/D-LPBI-DVA is proposed. The experimental results show that our model is more reasonable and outperforms existing models. The solutions obtained allow the problem to have a better effect, thereby effectively optimizing the hybrid energy output and achieving the multi-objective optimization requirements of low cost, low pollution, and high robustness of the system power supply.