Assessment of the Influence of Low-Carbon Technologies on the Operation of Distribution Networks

Abstract

The depletion of fossil fuels and the need to minimise carbon emissions have led to the adoption of low-carbon technologies (LCTs), such as variable renewable energy sources, electric vehicles (EVs), and heat pumps (HPs), which are integrated into electrical power systems via distribution networks (DNs). These LCTs introduce highly dynamic load profiles, which complicate the accurate estimation of net load in DNs and increase the risk of network constraint violations if not effectively controlled. The first part of this thesis analyses the steady-state operation of DN with different penetration levels of various LCTs using a Monte Carlo-based probabilistic load flow analysis. The analysis assesses the magnitude and severity of operational challenges, focusing on bus voltages, line loadings, and power losses based on the size and location of LCTs. The second part of the thesis presents a method for calculating the day-ahead operating active and reactive power envelopes for DNs with high LCT penetration. The method uses Latin Hypercube Sampling-based probabilistic load flow simulation and convex hull estimation to estimate operating envelopes (OEs) of the buses in DN, ensuring that buses’ operations within the OEs do not violate network constraints. The OEs are calculated considering a DN with different types of distributed energy resources (DERs), including solar photovoltaics, wind farms, and battery energy storage systems (BESSs), to analyse their dependence on DER capacity and output. The influence of EVs and HPs on the OEs is examined, leading to the calculation of risk-based OEs, introduced for the first time in this thesis, which consist of zero-risk and risk regions, to account for the uncertainty of EV and HP demands. Using the Morris screening method, the sensitivity of buses’ OEs to sizes and locations of DER is evaluated. The thesis identifies a key limitation of existing OEs: their inability to guarantee secure operation when all buses simultaneously operate at the edges of their OEs. To address this, risk operating zones within the OEs are calculated to indicate the risk of violating network constraints when buses simultaneously operate within the same zone. The final part of the thesis proposes the novel methodology for integrating OEs in optimisation studies. The proposed envelope calculation method is augmented with a particle swarm optimisation algorithm to determine the optimal BESSs’ locations and operating powers to minimise the power import from the transmission network and load shedding in DN over 24 hours. The calculation of OEs enables the DNO to maintain secure network operation without requiring precise knowledge of the actual net load at each bus, given that buses operate within their assigned OEs. This research also helps the DNO efficiently utilise the resources available in the DN to optimise network operation before resorting to expensive solutions such as infrastructure expansions.

Date of Award	10 Jun 2025
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Jovica Milanovic (Supervisor) & Alessandra Parisio (Supervisor)