Integration in Low - Voltage Feeders with Demand Response

The rapid growth of photovoltaics (PV) over the last few years has introduced many new challenges for power system operators. Most PV is integrated into the low-voltage network, where they can cause voltage and line constraint violations if generation exceeds local demand. This thesis explores the capabilities of using demand response to aid the integration of photovoltaic capacity into low voltage distribution networks. The charging schedule of electric water heaters is optimized and shifted to coincide with peak generation periods in an effort to increase the PV hosting capacity of a 10-node feeder supplying 400 households. The objective of the project is to maximize PV penetration while limiting the maximum system voltage rise to 3 %. The secondary goal of the control algorithm is to minimize the number of measurement points required. The effects of finite controllability are explored (i.e. how costs and energy shed increase if loads and PV installations can only be turned on or off in groups). PV forecast inaccuracies are taken into account through a model predictive control process, and results are compared to both deterministic optimization and the performance of a basic on-off controller acting on real time data. Lastly, the effects of switching frequencies are analyzed. Results show that demand response is able to increase the PV hosting capacity of the line to 34.2 %, while grouping control signals, forecasting errors and reduced switching have the potential of reducing this optimal behaviour on the order of 20 %.