  This paper proposes a novel cost-reflective and computationally efficient
method for allocating distribution network costs to residential customers.
First, the method estimates the growth in peak demand with a 50% probability of
exceedance (50POE) and the associated network augmentation costs using a
probabilistic long-run marginal cost computation based on the Turvey
perturbation method. Second, it allocates these costs to customers on a
cost-causal basis using the Shapley value solution concept. To overcome the
intractability of the exact Shapley value computation for real-world
applications, we implement a fast, scalable and efficient clustering technique
based on customers' peak demand contribution, which drastically reduces the
Shapley value computation time. Using customer load traces from an Australian
smart grid trial (Solar Home Electricity Data), we demonstrate the efficacy of
our method by comparing it with established energy- and peak demand-based cost
allocation approaches.
