Principal Investigator: Dr. Lingfeng Wang
With the increasing deployment of emerging technologies including Internet of things (IoTs) and artificial intelligence (AI) in modern power systems, smart monitoring (SM) of critical energy assets is becoming more realistic and is attracting more attention recently for compelling practical grid applications such as real-time health diagnosis and asset management. Such proactive monitoring technologies are expected to be highly beneficial to the power grid in both technical and economic aspects. In this project, we will investigate and quantify the potential technical and economic benefits brought about by the massive deployment of smart monitoring technologies in the evolving energy infrastructure. The impacts of adopting SM technologies in various power system equipment such as transformers and substations will be quantified at both the component and system levels in terms of reliability and resiliency. Also reliability/resiliency worth (i.e., cost-benefits) analysis will be performed to decide the optimal investment strategies on smart monitoring technologies. The quantitative impact studies of deploying condition assessment on both renewables-rich transmission and distribution systems will be conducted in planning and operational time horizons. The work is expected to be very useful to support risk-driven decision-making for preserving energy assets by upgrading power grids under the ongoing smart grid initiative. The major research tasks to be performed in this project are listed as follows:
1) A high-confidence reliability model will be developed to quantify and valuate the implications of smart monitoring on power supply reliability. A comprehensive set of probabilistic reliability indices including loss of load expectation (LOLE) and expected energy not supplied (EENS) will be derived and compared.
2) A holistic resiliency model will be developed to quantify and valuate the impacts of smart monitoring on power system resiliency in the face of extreme events by taking preventive, proactive measures. Informative resiliency metrics will be defined and calculated for various extreme event situations.
3) Criticality of power system components including transformers and substations will be studied to inform smart monitoring investments for maximizing the improvements of power system reliability and resiliency considering a broad range of uncertainties such as intermittent renewables and random equipment failures. Comprehensive cost-benefit analysis will also be conducted to provide multiple investment options to decision-makers.
4) All studies involving reliability/resiliency models and evaluation methods will be carried out for both renewables-rich transmission systems and distribution systems. The implications of deploying ubiquitous condition monitoring technologies in power grids will be explored in both short-term operational (e.g., dynamic thermal ratings, T&D coordination) and long-term planning (e.g., T&D expansion) respects. Also case studies will be performed for representative modern power grids at both transmission and distribution levels.
This project is expected to be beneficial to informing TSOs/ISOs, utilities & DSOs, and electric cooperatives in leveraging leading-edge condition monitoring technologies to manage and preserve their critical energy assets. It is a needed study for elevating power system planning/operations and improving decision making with emerging situational-awareness-enhancing technologies through uncertainty quantification. This study will provide a generic framework for the quantification and valuation of SM technologies in power grid applications based on their technical (e.g., reliability and resiliency), economic, and actuarial implications. With effective real-time condition assessment and long-term lifetime prediction of critical equipment, more informed decisions could be made in the time horizons of both system planning and operations to integrate higher amounts of power electronics based renewable energy resources into the power grid.