Security constrained generation scheduling for grids incorporating wind, photovoltaic and thermal power

• Security constrained generation scheduling problem for grids including thermal, wind and photovoltaic units is formulated.
• The uncertain nature of wind and PV powers are included by using Weibull probability distribution function.
• An adaptive hybrid technique comprising of artificial neural network with genetic algorithm and a priority list is proposed.
• Incorporating a 5% load-forecast error into the ANN training data effectively improves the scheduling performance.
• The results show that the wind and PV power plants can be integrated into the power grid more effectively.

In this paper, security constrained generation scheduling (SCGS) problem for a grid incorporating thermal, wind and photovoltaic (PV) units is formulated. The formulation takes into account the stochastic nature of both wind and PV power output and imbalance charges due to mismatch between the actual and scheduled wind and PV power outputs. A hybrid technique in which the basic elements are a genetic algorithm (GA) with artificial neural network (ANN) and a priority list (PL) is used to minimize the total operating costs while satisfying all operational constraints considering both conventional and renewable energy generators. Numerical results are reported and discussed based on the simulation performed on the IEEE 24-bus reliability test system. The results demonstrate the efficiency of the proposed approach to reduce the total production cost for real time operation. Moreover, the results verified that the proposed approach can be applied to different problem dimensions and can score more favorably compared with analytical techniques.