科研成果 by Year: 2020

Modeling and optimization of a large-scale urban energy-water nexus system with sufficient spatial resolutions is a complex challenge. By proper clustering technique, a large-scale problem could possibly be divided into small ones with high spatial resolution and accuracy. Existing literature tends to lower the complexity of large-scale urban energy system problem by accumulating demand profiles on the spatial dimension. This study proposes a flexible clustering approach based on density clustering method with combined index assessment process. The flexible approach considers not only the spatial dimensions but also the complementarity effect of different demand profile and control the computational time of system design and optimization. The approach can increase the clustering flexibility by providing more clustering options than conventional method, take advantages of complementarity effect to further improve the system economic performance and control the solving time in an acceptable range. The proposed approach is evaluated by a case study of a new business district in Shanghai, China with a proposed future energy-water nexus system. After three combined index assessment, 45 new clustering maps are generated by the flexible clustering approach and the final optimal solution obtained by the proposed approach can further obtain 6.74% cost savings compared with conventional clustering approach.

The decarbonisation of energy provision is key to managing global greenhouse gas emissions and hence mitigating climate change. Digital technologies such as big data, machine learning, and the Internet of Things are receiving more and more attention as they can aid the decarbonisation process while requiring limited investments. The orchestration of these novel technologies, so-called cyber-physical systems (CPS), provides further, synergetic effects that increase efficiency of energy provision and industrial production, thereby optimising economic feasibility and environmental impact. This comprehensive review article assesses the current as well as the potential impact of digital technologies within CPS on the decarbonisation of energy systems. Ad hoc calculation for selected applications of CPS and its subsystems estimates not only the economic impact but also the emission reduction potential. This assessment clearly shows that digitalisation of energy systems using CPS completely alters the marginal abatement cost curve (MACC) and creates novel pathways for the transition to a low-carbon energy system. Moreover, the assessment concludes that when CPS are combined with artificial intelligence (AI), decarbonisation could potentially progress at an unforeseeable pace while introducing unpredictable and potentially existential risks. Therefore, the impact of intelligent CPS on systemic resilience and energy security is discussed and policy recommendations are deducted. The assessment shows that the potential benefits clearly outweigh the latent risks as long as these are managed by policy makers.