Resilient power grid for smart city

Modern power grid has a fundamental role in the operation of smart cities. However, high impact low probability extreme events bring severe challenges to the security of urban power grid. With an increasing focus on these threats, the resilience of urban power grid has become a prior topic for a modern smart city. A resilient power grid can resist, adapt to, and timely recover from disruptions. It has four characteristics, namely anticipation, absorption, adaptation, and recovery. This paper aims to systematically investigate the development of resilient power grid for smart city. Firstly, this paper makes a review on the high impact low probability extreme events categories that influence power grid, which can be divided into extreme weather and natural disaster, human-made malicious attacks, and social crisis. Then, resilience evaluation frameworks and quantification metrics are discussed. In addition, various existing resilience enhancement strategies, which are based on microgrids, active distribution networks, integrated and multi energy systems, distributed energy resources and flexible resources, cyber-physical systems, and some resilience enhancement methods, including probabilistic forecasting and analysis, artificial intelligence driven methods, and other cutting-edge technologies are summarized. Finally, this paper presents some further possible directions and developments for urban power grid resilience research, which focus on power-electronized urban distribution network, flexible distributed resource aggregation, cyber-physical-social systems, multi-energy systems, intelligent electrical transportation and artificial intelligence and Big Data technology

Optimal dispatch of regional electricity-thermal system with multi-energy storage based on source and load forecasting

With the continuous development of the new power system construction， the installed capacity of wind power， photovoltaic and other renewable energies has been further increased. The randomness and uncertainty of source and load bring great challenges to the operation of the power system. Therefore， an optimal dispatch method of energy storage in regional electricity-thermal system based on multi-energy source and load forecasting， which improves the accommodation level of photovoltaic by the flexible adjustment ability of multi-energy storage and electricity-thermal coupling characteristics of the system. Firstly， the photovoltaic output and electricity-thermal load are forecasted based on the improved extreme learning machine and K-means clustering algorithm. Then， the conditional value at risk is applied to quantize the impact of uncertainty of photovoltaic output on operation cost. With target of minimizing the weighting sum of operation cost and conditional value at risk， the optimized operation scheduling model of district electricity-thermal system， containing battery， flywheel energy storage and heat storage tank， is established. The regulation requirement for flexible operation of the electricity-thermal system can be achieved by multi-energy storage. Finally， the case study is conducted to demonstrate the effectiveness of the proposed method