Network tariff design with flexible customers: Ex-post pricing and a local network capacity market for customer response coordination

This paper assesses the performance of differently implemented forward-looking network tariff designs in a future with many flexible customers. Theoretically, forward-looking approaches provide more cost-reflective signals than historical accounting approaches; they reveal to customers the trade-off between consuming more flexibly and long-term network expansion costs. We test forward-looking network tariff designs with higher locational differentiation, potentially leading to increased cost-reflectivity. However, we observe that if large shares of customers synchronize their responses to ex-ante-determined highly time-varying and locational-specific network charges, the peak-shifting effect materializes. Consequently, reinforcements occur earlier than envisioned. This scenario is plausible in an electrified and automatized future. Regulators should acknowledge this potential issue and consider to what extent respecting the predictability principle can offset economic efficiency in such a context. Ex-post pricing aligns network charges with long-term incremental network costs. But ex-post pricing has downsides, such as lower predictability for customers. To increase pre-dictability, we propose and test an innovative coordination mechanism in a case study. The coordination mechanism is a local network capacity market where customers book their expected network capacity usage in a competitive framework. Retailers/aggregators are foreseen as key actors translating complex tariffs into diverse products according to customers' flexibility and risk aversion.

Automated summary

This paper assesses the performance of differently implemented forward-looking network tariff designs and proposes an innovative coordination mechanism to increase predictability in a future with many flexible customers. The study reveals that if large shares of customers synchronize their responses to highly time-varying and locational-specific network charges, it can lead to unexpected reinforcements.