  Large-scale integration of renewable energy sources (RES) brings huge
challenges to the power system. A cost-effective reserve deployment and
uncertainty pricing mechanism are critical to deal with the uncertainty and
variability of RES. To this end, this paper proposes a novel locational
marginal pricing mechanism in day-ahead market for managing uncertainties from
RES. Firstly, an improved multi-ellipsoidal uncertainty set (IMEUS) considering
the temporal correlation and conditional correlation of wind power forecast is
formulated to better capture the uncertainty of wind power. The dimension of
each ellipsoidal subset is optimized based on a comprehensive evaluation index
to reduce the invalid region without large loss of modeling accuracy, so as to
reduce the conservatism. Then, an IMEUS-based robust unit commitment (RUC)
model and a robust economic dispatch (RED) model are established for the
day-ahead market clearing. Both the reserve cost and ramping constraints are
considered in the overall dispatch process. Furthermore, based on the
Langrangian function of the RED model, a new locational marginal pricing
mechanism is developed. The uncertainty locational marginal price (ULMP) is
introduced to charge the RES for its uncertainties and reward the generators
who provide reserve to mitigate uncertainties. The new pricing mechanism can
provide effective price signals to incentivize the uncertainty management in
the day-ahead market. Finally, the effectiveness of the proposed methods is
verified via numerous simulations on the PJM 5-bus system and IEEE 118-bus
system.
