Deep Learning Based Forecasting-Aided State Estimation in Active Distribution Networks

Bikash C. Pal; Firdous Ul Nazir; Malek Alduhaymi; Ravindra Singh

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Deep Learning Based Forecasting-Aided State Estimation in Active Distribution Networks

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arxiv 2310.13817 v1 pith:6PNLWBWW submitted 2023-10-20 eess.SY cs.SY

Malek Alduhaymi , Ravindra Singh , Firdous Ul Nazir , Bikash C. Pal This is my paper

classification eess.SYcs.SY

keywords demandactivebehaviourdistributionestimationnetworkstateabsence

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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.

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