Probabilistic Impact Assessment of Network Tariffs in Low Voltage Distribution Networks

In this paper, we present a probabilistic framework to assess the impacts of different network tariffs on the consumption pattern of electricity consumers with distributed energy resources such as thermostatically controlled loads and battery storage; and the resultant effects on the distribution network. A mixed integer linear programming-based home energy management system with implicit modeling of peak demand charge is used to schedule the controllable devices of residential customers connected to a low voltage network in order to analyze the impacts of \textit{energy-} and \textit{demand-based tariffs} on network performance. The simulation results show that flat tariffs with a peak demand component perform best in terms of electricity cost reduction for the customer, as well as in mitigating the level of binding network constraints. This is beneficial for distribution network service providers where there is high PV-battery penetration.