  Operating an active distribution network (ADN) in the absence of enough
measurements, the presence of distributed energy resources, and poor knowledge
of responsive demand behaviour is a huge challenge. This paper introduces
systematic modelling of demand response behaviour which is then included in
Forecasting Aided State Estimation (FASE) for better control of the network.
There are several innovative elements in tuning parameters of FASE-based,
demand profiling, and aggregation. The comprehensive case studies for three UK
representative demand scenarios in 2023, 2035, and 2050 demonstrated the
effectiveness of the proposed approach.
