| dc.description.abstract | Micro energy grid (MEG) is widely used to meet the combined electricity, heating, cooling and natural gas demands for numerous customers’ types. Design of MEGs were extensively introduced in numerous articles, however safety analysis methods for MEG design are not existing so far. This study develops a hazard and operability (HAZOP) matrix for MEGs by proposing a resilience matrix. In addition, it proposes two advanced risk-modeling approaches, namely fault tree and layer of resilience analysis (LORA), for risk and resilience analysis of MEG. Selected independent resilience layers (IRLs) were proposed to achieve a resilient MEG by increasing safety integrity level (SIL). IRLs are applied using co-generation and thermal energy storage (TES) technologies to mitigate the hazards of system failure, increase efficiency, and minimize greenhouse gas emissions. The proposed risk assessment approach aims to design a resilient MEG that has the ability to deal with those potentials efficiently. In addition, an energy risk analysis has been applied to each MEG’s physical domains such as electrical, thermal, mechanical and chemical. These concurrent objectives lead to achieving higher resilience, fewer greenhouse gases emissions, and greater sustains economy. A multi-level hierarchical decision making (MLHDM) is one of the IRLs that are proposed in this study. It aims to boost the MEG’s self-healing features on risks uncertainty of the system operation. The structural design of MLHDM consists of three concurrent levels functioning together to achieve a resilient operation. The simulation results of the proposed resilient MEG infrastructure that combine a selected group of IRLs, shows the ability to work with high level of self-healing capability under various hazardous scenarios as well as meeting the on-demand energy requirement. On the other hand, intelligent reasoning algorithms using Bayesian belief network (BBN) are proposed to accurately and instantaneously estimate risks in MEG. This study introduces a hybridsafety assessment approach for MEG diagnosis by using a combination of ANFIS and BBN techniques. Finally, the validation results of the proposed safety analysis tools reveal promising solution for designing resilient MEGs. | en |
