A Turvey-Shapley Value Method for Distribution Network Cost Allocation

TL;DR

This paper introduces a new cost allocation method for distribution networks that combines probabilistic peak demand estimation with a scalable Shapley value approach, improving accuracy and computational efficiency.

Contribution

It develops a novel probabilistic long-run marginal cost method integrated with a scalable Shapley value-based allocation for distribution network costs.

Findings

Efficient clustering reduces Shapley computation time significantly.

The method provides more accurate cost allocation compared to traditional approaches.

Demonstrated effectiveness using Australian smart grid data.

Abstract

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.