Flexibility Integration and Pricing Strategy for Distribution Networks to Unlock Spare Capacity

Abstract

Abstract
The electrification of key sectors, such as transportation, heating, and industry, is critical to reducing greenhouse gas emissions and achieving decarbonization goals. This shift, coupled with the rapid integration of renewable energy sources, is transforming power systems and amplifying the role of distribution networks. As the energy system moves toward a decentralized, digitalized, and decarbonized model, innovative approaches are required to integrate flexible customers efficiently and reliably.

In the UK, the increasing penetration of renewable generation and flexible demand are intensifying the pressure on distribution networks, which have traditionally relied on costly infrastructure upgrades to manage capacity. However, significant unused capacity exists within these networks—with typical utilization rates of only 50-60% due to conservative N-1 planning standards—yet effective price signals to encourage flexible customer participation are still lacking.

This thesis presents a framework for integrating flexible customer demands into distribution networks, aiming to enhance network efficiency and defer costly infrastructure investments. The framework comprises three integrated components with the following key contributions:

First, a mixed flexibility allocation model is introduced, which maximizes network utilization by enabling flexible customers to access spare capacity through the selective relaxation of N-1 security criteria. Case studies demonstrate network utilization improvements of over 250% without compromising the reliability of traditional customers, thereby reducing the need for new network investments.

Second, a Multi-Attribute Utility Theory (MAUT) framework is developed to systematically evaluates and ranks customer flexibility contributions based on response time, flexibility magnitude, and duration. The analysis reveals that duration dominates the utility weighting (64-99% across customer categories), with commercial customers achieving the highest flexibility scores (up to 2.204). This enables network operators to effectively prioritize high-value flexibility resources.

Third, a dynamic pricing model based on Stackelberg game theory is proposed, in which pricing signals incentivize load reductions during peak periods while incorporating customer-specific Value of Lost Load (VoLL). The model successfully manages congestion through exponential pricing activation beyond 80% network utilization, effectively balancing network requirements with customer incentives and achieving a stable equilibrium within 15-20 iterations.

The integrated framework demonstrates that strategic deployment of flexible demand can significantly enhance distribution network efficiency while maintaining system reliability, thereby providing a practical pathway toward more sustainable and cost-effective network management.

Date of Award	8 Oct 2025
Original language	English
Awarding Institution	University of Bath
Supervisor	Furong Li (Supervisor) & Chenghong Gu (Supervisor)