Situation Awareness for Smart Distribution Systems

• Andere auteurs: Ge, Leijiao (Redacteur), Yan, Jun (Redacteur), Sun, Yonghui (Redacteur), Wang, Zhongguan (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Onderwerpen: Technology: general issues; History of engineering & technology; community integrated energy system; energy management; user dominated demand side response; conditional value-at-risk; electric heating; load forecasting; thermal comfort; attention mechanism; LSTM neural network; smart distribution network; situation awareness; high-quality operation and maintenance; critical technology; comprehensive framework; distributionally robust optimization (DRO); integrated energy system (IES); joint chance constraints; linear decision rules (LDRs); Wasserstein distance; load disaggregation; denoising auto-encoder; REDD dataset; TraceBase dataset; machine learning; secondary equipment; CNN; short text classification; electric vehicle; short-term load forecasting; convolutional neural network; temporal convolutional network; climate factors; correlation analysis; sustainable wind-PV-hydrogen-storage microgrid; power-to-hydrogen; receding horizon optimization; storage; photovoltaic (PV) system; DC series arc fault; power spectrum estimation; attentional mechanism; lightweight convolutional neural network; capacity configuration; wind-photovoltaic-thermal power system; carbon emission; multi-objective optimization; inertia security region; n/a
• Online toegang: DOAB: download the publication; DOAB: description of the publication

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed ge...