Predictive power dispatch through negotiated locational pricing

Abstract

A predictive mechanism is proposed in order to reduce price volatility linked to large fluctuations from demand 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.