An interval optimization based day-ahead scheduling scheme for renewable energy management in smart distribution systems

The integration of renewable energy generation into distribution systems has a significant influence on network power losses, nodal voltage profile and security level due to the variability and uncertainty of renewable energy generation. This paper proposes a novel interval optimization based day-ahead scheduling model considering renewable energy generation uncertainties for distribution management systems. In this approach, the forecasting errors of wind speed, solar radiation intensity and loads are formulated as interval numbers so as to avoid any need for accurate probability distribution. In this model, the total nodal voltage deviation and network power losses are optimized for the economic operation of distribution systems with improved power quality. Consequently, the order relation of interval numbers is used to transform the proposed interval optimal scheduling model into a deterministic optimization problem which can then be solved using the harmony search algorithm. Simulation results on 33-node and 119-node systems with renewable energy generation showed that considerable improvements on system nodal voltage profile and power losses can be achieved with multiple interval sources of uncertain renewable energy generation and loads.