Abstract
Real-time thermal ratings (RTTRs) is an emerging technique used to calculate the rating of electrical conductors based on local, real-time weather conditions; this leads to an increased rating with respect to conventional approaches the majority of the time, and can be used to increase the energy yield of distributed generators, support the network during outages and defer network reinforcement. Unfortunately it is not presently recognised in network planning, design and security of supply standards. This represents a barrier to utilising RTTRs in power networks, and must be addressed. This study presents a new, probabilistic method for accounting for the variable ratings during network planning. This is coupled with an analysis of the risk of being unable to supply customers in a network adopting variable ratings, compared with the risk in the same network using conventional ratings; hence the method proposed in this study allows additional load to be connected to the network at a quantified level of risk. Finally, this method could be applied to other emerging network technologies and techniques such as demand side management or energy storage.
Highlights
Electrical networks are undergoing a fundamental change in function and operation
This paper examines the contribution of one such technique, Real-Time Thermal Ratings (RTTR), using the Expected Energy Not Supplied (EENS) by a Perfect Circuit method, and proposes an alternative probabilistic method
The confidence values corresponding to the contributions suggested by the Equivalent EENS method are very low; this illustrates how inappropriate that method is for RTTR
Summary
Electrical networks are undergoing a fundamental change in function and operation. As a result of decarbonisation targets [1], electricity consumption is predicted to increase to accommodate electric heating and transport, with some studies suggesting electricity demand could as much as double. Underground cables and power transformers can be operated at an elevated rating on a cyclic basis thanks to their high thermal time constants [13, 14], and could be coupled with overhead lines utilising RTTR This is true for security of supply, when the increased ratings will only be relied on during a contingency. RTTR can be deployed in other countries, and while its effectiveness will be affected by local climate and demand patterns [21], the methods presented in this paper are still applicable This is because analysis carried out in developing the new probabilistic method was done considering the fundamental problem of line ratings, and how it relates to security of supply for customers, rather than from the perspective of the existing standards.
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