## 5 Discussion of Simulations and Results
### Comparative Study
No matter which of the three sample states, in Table 5, the order of final SWs in the three scenarios from high to low is Scenario 1 (6177.1 ($/h)), Scenario 2 (6108.2 ($/h)), and Scenario 3 (6063.4 ($/h)). In Table 6, the order of final SWs in the three scenarios from high to low is Scenario 1 (5172.2 ($/h)), Scenario 2 (5123.6 ($/h)), and Scenario 3 (5102.5 ($/h)). In Table 7, the order of the final SWs in the three scenarios from high to low is: Scenario 1 (4639.1 ($/h)), Scenario 2 (4610.7 ($/h)), and Scenario 3 (4562.7 ($/h)). Moreover, although we cannot compare final SWs in Scenarios 1, 2 and 3 for every state point due to the continuous state space, the phenomenon that the order of the final SWs in the three scenarios from high to low are Scenario 1, Scenario 2, and Scenario 3 can actually be found under every state point of the \(21\times 31\) sample states in \(\mathbf{Sap}\). As mentioned in Section 5.1, every participant in Scenario 3 is a fuzzy Q-learning-based agent; NRGenCO1 in Scenario 2 is a fuzzy Q-learning-based agent while all the other participants in Scenario 2 are our proposed GDCAC-based ones, and every participant in Scenario 1 is our proposed GDCAC-based agent. Therefore, it can, to some extent, be verified that with the increase in the number of participants applying our proposed approach as their bidding decision-making tool, SW in the market increases. From Table 8, it can be seen that:  
1. The order of final average AVLMPs of \(21\times 31\) sample states in the three scenarios, from low to high, is: Scenario 1 (37.0352 ($/MWh)), Scenario 2 (37.4531 ($/MWh)), and Scenario 3 (38.2237 ($/MWh)), which, to some extent, verifies the renewable power penetration in EM. The final average AVLMP of the \(21\times 31\) sample states will be lowered by increasing the number of GDCAC-based agents. 2. The order of final average SWs of \(21\times 31\) sample states in the three scenarios from high to low is: Scenario 1 (5329.5 ($/h)), Scenario 2 (5280.8 ($/h)), and Scenario 3 (5242.9 ($/h)), which, to some extent, verifies the renewable power penetration in EM. The final average SW of \(21\times 31\) sample states will be increased by increasing the number of GDCAC-based agents. 3. Increasing SW as well as lowering clearing prices (represented by average AVLMPs) stands for the economic efficiency improvement in the whole market, which is proven, to some extent and through this comparative study, to be attributable to our proposed GDCAC approach. Therefore, from the abovementioned analysis of Tables 5-8, it is concluded that in bilateral hour-ahead EM with renewable power penetration, our proposed GDCAC approach is superior to the fuzzy Q-learning one from the perspective of economics. That is mainly because: (1) although both state spaces in the two EM modeling approaches are continuous, the action set of every agent in fuzzy Q-learning approach must be discrete, which is not the same as all continuous action spaces in the GDCAC approach; and (2) the phenomenon of discrete action sets makes it harder for an agent to obtain globally optimal actions. Moreover, although it was verified in [2] that the fuzzy Q-learning approach is superior to the Q-learning one in EM modeling considering renewable power penetration, Table 9 still lists some simulation results obtained - from the GDCAC, fuzzy Q-learning, and Q-learning approaches after 20,000 iterations, respectively. In Table 9, simulation with the GDCAC approach is equivalent to simulation with Scenario 1, simulation with the fuzzy Q-learning approach is equivalent to simulation with Scenario 3, and simulation with Q-learning approach assumes every strategic participant in EM is the Q-learning-based agent with the same discrete action sets and relevant parameters as the fuzzy Q-learning approach (listed in Table 3) and with the same discrete state set as _Sap_ for the purpose of comparison.