To encourage the use of new energy (NE), tackle the challenges posed by measurement harmonics, and achieve precise perception and forecasting of the power system’s state following the integration of substantial renewable energy sources, this paper introduces a novel method for interval state situation awareness in power systems, incorporating the unpredictability of photovoltaic (PV) output forecasting and adaptive strategies for handling full-measurement harmonics. Firstly, by leveraging historical data from PV power plants within the same geographical area, this method constructs a spatiotemporal correlation model that factors in the uncertainty associated with PV output predictions. Subsequently, by amalgamating full-measurement harmonics with uncertainty metrics and interval principles, where the uncertainty metric defines the distance margin between the interval’s upper and lower bounds and the actual value, an uncertainty measure interval state model is developed for the adaptive management of full-measurement harmonics. Following this, a power system interval state situation awareness method is proposed, grounded in exponential objective function (EOF) state estimation, which accounts for both the unpredictability of PV output forecasting and the adaptive handling of full-measurement harmonics. Finally, the method’s effectiveness is rigorously validated through comprehensive simulations on the IEEE 39-bus test system, demonstrating significant improvements in both renewable energy utilization and power quality maintenance.
ZHOU et al. (Thu,) studied this question.