  Demand response provides utilities with a mechanism to share with end users
the stochasticity resulting from the use of renewable sources. Pricing is
accordingly used to reflect energy availability, to allocate such a limited
resource to those loads that value it most. However, the strictly competitive
mechanism can result in service interruption in presence of competing demand.
To solve this issue we investigate on the use of forward contracts, i.e.,
service level agreements priced to reflect the expectation of future supply and
demand curves. Given the limited resources of microgrids, service interruption
is an opposite objective to the one of service availability. We firstly design
policy-based brokers and identify then a learning broker based on artificial
neural networks. We show the latter being progressively minimizing the
reimbursement costs and maximizing the overall profit.
