  Increasing wind turbines (WT) penetration and low carbon demand can
potentially lead to two different flow peaks, generation and load, within
distribution networks. This will not only constrain WT penetration but also
pose serious threats to network reliability. This paper proposes energy storage
(ES) to reduce system congestion cost caused by the two peaks by sending
cost-reflective economic signals to affect ES operation in responding to
network conditions. Firstly, a new charging and discharging (C/D) strategy
based on Binary Search Method is designed for ES, which responds to system
congestion cost over time. Then, a novel pricing method, based on Location
Marginal Pricing, is designed for ES. The pricing model is derived by
evaluating ES impact on the network power flows and congestion from the loss
and congestion components in Location Marginal Pricing. The impact is then
converted into an hourly economic signal to reflect ES operation. The proposed
ES C/D strategy and pricing methods are validated on a real local Grid Supply
Point area. Results show that the proposed Location Marginal Pricing-based
pricing is efficient to capture the feature of ES and provide signals for
affecting its operation. This work can further increase network flexibility and
the capability of networks to accommodate increasing WT penetration.
