A predictive mechanism is proposed in order to reduce price volatility linked to large fluctuations from de- mand and renewable energy generation in competitive electricity markets. The market participants are modelled as price-elastic units, price-inelastic units, and storage operators. The distributed control algorithm determines prices over a time horizon through a negotiation procedure in order to maximize social welfare while satisfying network constraints. A simple flow allocation method is used to assign responsibility for constraint violations on the network to individual units and a control rule is then used to adjust nodal prices accordingly. Such a framework is appropriate for the inclusion of aggregated household appliances or other ‘virtual’ market participants realized through smart grid infrastructure. Results are examined in detail for a 4-bus network and then success is demonstrated for a densely-populated 39-bus network. Formal convergence requirements are given under a restricted subset of the demonstrated conditions. The scheme is shown to allow storage to reduce price volatility in the presence of fluctuating demand.