TY - GEN
T1 - Risk-aware management of distributed energy resources
AU - Zhang, Yu
AU - Gatsis, Nikolaos
AU - Kekatos, Vassilis
AU - Giannakis, Georgios B.
PY - 2013
Y1 - 2013
N2 - High wind energy penetration critically challenges the economic dispatch of current and future power systems. Supply and demand must be balanced at every bus of the grid, while respecting transmission line ratings and accounting for the stochastic nature of renewable energy sources. Aligned to that goal, a network-constrained economic dispatch is developed in this paper. To account for the uncertainty of renewable energy forecasts, wind farm schedules are determined so that they can be delivered over the transmission network with a prescribed probability. Given that the distribution of wind power forecasts is rarely known, and/or uncertainties may yield non-convex feasible sets for the power schedules, a scenario approximation technique using Monte Carlo sampling is pursued. Upon utilizing the structure of the DC optimum power flow (OPF), a distribution-free convex problem formulation is derived whose complexity scales well with the wind forecast sample size. The efficacy of this novel approach is evaluated over the IEEE 30-bus power grid benchmark after including real operation data from seven wind farms.
AB - High wind energy penetration critically challenges the economic dispatch of current and future power systems. Supply and demand must be balanced at every bus of the grid, while respecting transmission line ratings and accounting for the stochastic nature of renewable energy sources. Aligned to that goal, a network-constrained economic dispatch is developed in this paper. To account for the uncertainty of renewable energy forecasts, wind farm schedules are determined so that they can be delivered over the transmission network with a prescribed probability. Given that the distribution of wind power forecasts is rarely known, and/or uncertainties may yield non-convex feasible sets for the power schedules, a scenario approximation technique using Monte Carlo sampling is pursued. Upon utilizing the structure of the DC optimum power flow (OPF), a distribution-free convex problem formulation is derived whose complexity scales well with the wind forecast sample size. The efficacy of this novel approach is evaluated over the IEEE 30-bus power grid benchmark after including real operation data from seven wind farms.
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U2 - 10.1109/ICDSP.2013.6622685
DO - 10.1109/ICDSP.2013.6622685
M3 - Conference contribution
AN - SCOPUS:84888863873
SN - 9781467358057
T3 - 2013 18th International Conference on Digital Signal Processing, DSP 2013
BT - 2013 18th International Conference on Digital Signal Processing, DSP 2013
T2 - 2013 18th International Conference on Digital Signal Processing, DSP 2013
Y2 - 1 July 2013 through 3 July 2013
ER -