Understanding the Benefits of Dynamic Line Rating Under Multiple Sources of Uncertainty

Abstract

Abstract--This paper analyses the benefits of dynamic line rating (DLR) in the system with high penetration of wind generation. A probabilistic forecasting model for the line ratings is incorporated into a two-stage stochastic optimization model. The scheduling model, for the first time, considers the uncertainty associated with wind generation, line ratings and line outages to co-optimize the energy production and reserve holding levels in the scheduling stage as well as the re-dispatch actions in the real-time operation stage. Therefore, the benefits of higher utilization of line capacity can be explicitly balanced against the costs of increased holding and utilization of reserve services due to the forecasting error. The computational burden driven by the modelling of multiple sources of uncertainty is tackled by applying an efficient filtering approach. The case studies demonstrate the benefits of DLR in supporting costeffective integration of high penetration of wind generation into the existing network. We also highlight the importance of simultaneously considering the multiple sources of uncertainty in understanding the benefits of DLR. Furthermore, this paper analyses the impact of different operational strategies, the coordination among multiple flexible technologies and installed capacity of wind generation on the benefits of DLR.

Optimal Dynamic Line Rating Forecasts Selection Based on Ampacity Probabilistic Forecasting and Network Operators’ Risk Aversion

“…For a risk-neutral approach, the ratio between reserve costs (0.085%) and benefits (1.01%), i.e. a ratio of 1 to 12, is close to that found in [22].…”

“…The problem to be solved is the optimal operation of a grid on which some lines are equipped with DLR. It is similar to that formulated in [18] and [22], which assess the DLR value in grid management. Since several uncertainties are involved, a stochastic optimisation approach is applied.…”

“…where Ng is a set of conventional generators; I g is a binary variable with value 1 describing a committed generator, and 0 if not; πfuel g is the fuel cost for generator g (€/MWh); πfix g is the commitment price for a conventional generator g (€/h); P g is the scheduled output of generator g (MW); πhup g and πhdo g are the costs for maintaining up and down reserve for a generator g (€/MWh); H g up and H g do are the up and down reserve service holding amounts for generator g (MW); N s is the set of potential future realizations of DLR and other stochastic variable observations, with each scenario having a probability ρ s of occurrence set with probabilistic forecasts; πrup g and πrdo g are the reserve activation costs (€/MWh); and R g,s up and R g,s do are the activated reserves from a generator g at scenario s (MW). The constraints are those of a DC power flow, and are described in [18] and [22]. Here, N-1 constraints are added to account for the risks of line failure, as proposed in [24], which investigates DLR use for transfer capacity setting.…”

Guest Editorial for the Special Section on Enabling Very High Penetration Renewable Energy Integration Into Future Power Systems

“…The results showed that the coordination between battery energy system and wind farms can significantly reduce the operation cost and renewable energy curtailment. F. Teng et al analyzed the benefits of dynamic line rating in the transmission system [13]. A scheduling model with consideration of multiple sources of uncertainty including wind generation, line ratings, and line outrages was proposed.…”

Improving DTR assessment by means of PCA applied to wind data

“…DTR bring us several benefits [4]. It allows the use of existing transmission assets efficiently, thus improving economic benefits, allows deferral of new transmission lines, it makes it possible to manage more easily contingency conditions.…”