Electricity Network Reliability Planning and Evaluation Considering Flexible Solutions

Abstract

Abstract-Amid contemporary electric power system planning and operating regimes, the transmission network is pervasively operated, and planned, with time-invariant thermal ratings (TITRs) of transmission assets. This results in a transmission network which is not fully (and efficiently) utilized. In the future, however, power systems will be increasingly expected to transition into a smart grid-in order to be more efficient and economical. Consequently, the network will pervasively have to be operated, and planned, with time-varying thermal ratings (TVTRs) of transmission assets. Moreover, the smart grid will further facilitate the inclusion of demand response (DR) into the transmission network planning process. Therefore, this paper aims to discuss the need for, and implications of, adopting novel electricity network reliability planning and evaluation methodical approaches which consider flexible solutions (i.e., DR and TVTR). Subsequently, in order to encourage the adoption of these proposed approaches, the quantification of the benefits from increased, efficient and economical transmission utilization considering flexible concepts such as non-ageing TVTR, controlled-ageing of transmissions assets due to advanced operation of TVTR, and DR within transmission system are performed-and evaluated. The IEEE 24-bus RTS is used under sequential Monte Carlo simulation (SMCS) modelling of the reliability evaluation process in order to validate the proposed methodological enhancements and to evaluate network performance.

Network reliability framework integrating demand response and flexible OHL ratings

“…where D is the conductor diameter in mm, k f is the air thermal conductivity, ρ f is the air density and μ f is the air viscosity, while T a (t) is the ambient temperature, V w (t) is the wind speed, k angle (t) is the wind angle on the conductor, at time t. Equation 21is restructured to (22), where K Qc2 is described by (25), while K Qc1 is modelled by (23) and (24) for high and low wind speeds, respectively. Hence, (22) allows to account for the base temperature T cmax , and associate Q c (t) with ΔT c (t) Q c (t) = K Qc1 ΔT c (t) + K Qc2 (22) K Qc1 = 1.01 + 0.0372…”

“…The EDR benefits for network reliability and the financial benefits for NOs and customers are also investigated in [23]. The added reliability benefits from DR when considering TVLR for OHLs are examined in [23][24][25]. So far, the combined benefits from utilising other thermal uprating methods than TVLR (e.g.…”

Contribution of emergency demand response and emergency loading of overhead lines in network resilience enhancements

“…Existing works have addressed the implementation of these flexibility methods on improving reliability [28–30], but none of them has considered the impact on resilience enhancement. Besides, despite EL and PTR enhance flexibility at zero implementation costs, they relax the thermal constraints and lead to extra ageing risks, while the works quantifying the provided flexibility versus network ageing risks resulted from EL and PTR are very limited [31, 32].…”