**6. Case Studies**

For purpose of testing the optimization performance of MCR-Q(λ) learning, the simulation results of Q(λ) learning, Q learning [41], quantum genetic algorithm (QGA) [42], GA [43], PSO [44], ant colony system (ACS) [45], group search optimizer (GSO) [46] and artificial bee colony (ABC) [47] were also introduced for comparison. Note that the weight coe fficient in Equation (5) can be adjusted according to the preference on di fferent components of the objective function. In the simulation analysis, since three components of the objective function in Equation (5) have the same preferences, and the weight coe fficient in Equation (5) is set to be 1/3, both the testing IEEE 118-bus system and IEEE 300-bus system are referenced from the tool called MATPOWER [48], in which the detailed parameters can be found in [49]. Besides, it assumes that both the wind and solar energy outputs can be accurately acquired by using e ffective forecasting techniques, e.g., the deep long-short-term memory recurrent neural network [50]. Among them, the algorithms are simulated and tested in Matlab 2016b by a personal computer with an Intel(R) Core TM i5-4210 CPU at 2.6 GHz with 8 GB of RAM.

#### *6.1. Case Study of IEEE 118-Bus System*
