Abstract/Summary

British Distribution Network Operators (DNOs) are facing challenges due to the energy sectors transitioning into a low carbon economy. This thesis aims to present novel methods to aid DNOs in operating their Low-Voltage (LV) networks despite this ongoing transition and its entailed challenges. The presented methods are realised with the use of Battery Energy Storage Solutions (BESS) and they develop BESS energy management algorithms whilst focusing on communication regimes and sub-half-hourly volatility in demand. Consequently, improving LV network operation mainly considers the reduction of peak power flow, but also includes reducing energy losses, voltage deviation, the magnitude of neutral currents and phase unbalance. Without these methods, DNOs would have to rely on traditional network reenforcements so that LV networks are kept within statutory voltage bands, for example. Extending current literature with methods to control a single energy resource and a distributed BESS - whilst considering requirements for communication systems that effect BESS control - is the main contribution of this thesis. The BESS control algorithm developed in this thesis is designed to incorporate half-hourly forecasts and sub-half-hourly load volatility. Resulting key network parameters and their interplay are identified and daily load peaks, caused by load volatility, could be reduced by an average of 3.8kW(from 45kW). Methods are developed and address challenges for controlling a single BESS. Neglected challenges are addressed in the subsequent BESS control methods where a desynchronised Multi-Agent Network (MAS) and communication-less BESS control fill this gap. Results show how internal algorithm behaviour changes when desynchronising the communication environment, but without impacting the global performance of the distributed BESS. Also, realtime performance of the communication less control algorithm is studied on different basis to show how effects from uncoordinated Low-Carbon Technologies (LTCs) like Electric Vehicle (EV) charging, can be successfully mitigated. All objectives aligning with the aforementioned achievements have been met and the comparable storage control techniques in literature are either met or exceeded in performance when subjected to the available datasets.