Dynamic Network Prices for Prosumer-aware Hosting Capacity Management

TL;DR

This work addresses hosting-capacity management in low-voltage networks with high DER penetration by designing dynamic network prices through a bilevel optimization framework. The outer level (DSO) sets revenue-driven tariffs under cost-recovery and network constraints, while the inner level (prosumers) minimizes electricity expenditure by optimally operating behind-the-meter DERs, including BESS and PV. The model is reformulated to a single-level problem via KKT conditions and solved centrally, demonstrating that price signals tied to local network conditions can reduce export peaks, improve reliability, and preserve consumer prerogatives. Case studies on a radial LV network show constraints such as voltage limits and line capacities are satisfied, with BESS enabling flexible responses and fair, uniform pricing across the network. Overall, the framework provides actionable insights for designing dynamic, customer-centric network tariffs and advancing hosting-capacity management under high DER uptake.

Abstract

The fast uptake of distributed energy resources (DERs) presents increasing challenges for managing hosting capacity in distribution networks. Existing solutions include direct load control, operating envelopes, and price-based control through dynamic energy prices. Despite their effectiveness, these methods often rely on assumed prosumer behavioural patterns and overlook prosumers' desire to retain control over their devices. Additionally, current fixed or Time-of-Use (ToU) prices are based on spatial and temporal averages, having limited impact on network conditions and DER operation. To address these limitations, this paper proposes a bilevel optimisation framework that explicitly models prosumer decision-making in the design of dynamic network prices. The upper level represents the distribution system operator (DSO), setting network prices under cost-recovery and network constraints, while the lower level models prosumers optimising DER operation in response. The proposed framework preserves customer prerogative, enhances DER flexibility, and offers actionable insights for network hosting capacity management and the evolution of network tariff structures under high DER penetration.

Figures (9)

• Figure 1: Composition of an average household electricity bill in the NEM.
• Figure 2: Composition of an example business electricity bill.
• Figure 3: Example of energy and network tariffs used in Australia.
• Figure 4: Overview of the proposed bilevel framework.
• Figure 5: Topology of the distribution network test system.