Advancements in Distribution System State Estimation: A Comprehensive Review of Methods, Challenges, and Smart-Grid Integration

Abstract

Distribution networks are operating under increasingly variable conditions as renewable generation and electric vehicles become more widespread. Maintaining reliable and secure operation in this setting requires timely information about the electrical state of the network, yet DSSE remains difficult to implement when measurement coverage is sparse, communication infrastructure is limited, and feeder configurations are complex enough to compromise observability. This review examines how current DSSE methods deal with these conditions, including high R/X ratios, three-phase unbalance, and limited instrumentation. More than one hundred studies are considered, spanning optimization-based estimators and related mean-squared-error formulations, Kalman-filter-based dynamic approaches, artificial intelligence techniques, and centralized and distributed estimation schemes. Their reported advantages, limitations, and application areas are assessed in the context of practical deployment requirements. The review also considers directions that combine physical network models with neural networks, improve pseudo-measurement generation, account for dynamic and stochastic behavior, and distribute the estimation process through approaches such as Bayesian consensus filtering and the alternating direction method of multipliers. Comparative tables summarize the main characteristics of these methods, while the discussion gives particular attention to real-time implementation, cybersecurity, and operational constraints encountered in modern distribution networks.