## 4 Discussion and Conclusions  
In this paper, we hypothesized that the real-time wholesale price of electricity (USD/MWh) in the ERCOT decreases in response to the number of active wind and solar applications; i.e., there is an association between the number of active wind and solar applications and the price of electricity in the ERCOT. Increased electricity demand throughout the ERCOT is first met by resources that are the cheapest to produce and deliver to the grid--the renewable resources, wind and solar. Hence, from a pricing perspective, the merit-order effect (MOE) of this dispatching protocol should contribute toward lowering real-time wholesale electricity prices throughout the state. Three key inferences resulted from our analysis. First, tax incentives that increase the number of active wind and solar facilities lead to a statistically significant (\(p<0.0001\)) reduction in wholesale electricity price (USD/MWh), ranging between 2.31% and 6.6% across the ERCOT during different hours of the day. Second, for a 10% increase in tax-incentivized green energy generation during a 24 h period, there is a statistically significant (\(p<0.0001\)) reduction in the production cost (USD/MWh), ranging between 0.82% and 1.96%. Finally, electricity price reductions from solar energy are much lower than those from wind generation and/or are not statistically significant. As the electricity market in Texas attempts to increase its dependence on green energy via friendly tax policies for the producers of renewable resources, the MOE of these resources will increase, thereby reducing real-time wholesale prices. While the statistical association between wind generation and wholesale pricing is unambiguous, the same is not true of solar generation. Complementing previous research, such as [PERSON] (2020) and [PERSON] et al. (2021), we find that, even with increased tax incentives, the MOE from solar energy is either negligible, or is not statistically significant ([PERSON] et al., 2021; [PERSON] et al., 2021). As described in previous sections, solar energy is still a growing resource in the ERCOT. The US Energy Information Administration (EIA) estimated that, between 2016 and 2017, the net solar generation in Texas rose from 96,000 MWh to 199,000 MWh, which only accounts for about 1.35% of the state's electricity needs. However, the EIA projects \begin{table}
\begin{tabular}{c c c c} \hline
**Skew-t Regression** & **Hour 3 AM** & **Hour 11 AM** & **Hour 4 PM** \\
**Independent Variables** & **(1)** & **(2)** & **(3)** \\ \hline WSapp18 & \(-0.0309\)* & \(-0.0395\)* & \(-0.0542\)* \\  & (0.0029) & (0.0050) & (0.0057) \\ Hprice1 & \(-2.7518\)* & - & - \\  & (0.0084) & - & - \\  & 0.5908 * & & \\  & (0.0194) & - & - \\  & & 0.8121 * & - \\  & & (0.0179) & - \\  & & & 1.3444 * \\  & - & - & (0.0179) \\ Ln(NGprice) & 0.6336 * & 0.5617 * & 0.5193 * \\  & (0.0099) & (0.0110) & (0.0118) \\  & \(-0.0824\)* & \(-0.0666\)* & \(-0.0717\)* \\  & (0.0021) & (0.0019) & (0.0025) \\ Ln(Sgen) & - & \(-0.0015\)* & 0.0145 * \\  & (0.0026) & (0.0025) \\ Ln(Ngen) & \(-0.1760\)* & \(-0.1695\)* & \(-0.1967\)* \\  & (0.0074) & (0.0097) & (0.0109) \\ Ln(Hload) & \(-0.1050\)* & 0.3545 * & 0.6220 * \\  & (0.0135) & (0.0208) & (0.0227) \\ Ln(REload) & 0.3663 * & 0.3485 * & 0.3252 * \\  & (0.0131) & (0.0233) & (0.0213) \\ Constant & 1.7155 * & \(-1.8098\)* & \(-3.4962\)* \\  & (0.0748) & (0.1020) & (0.1017) \\ \hline \end{tabular} Legend: * \(p<0.0001\).  
\end{table} 
Table 6: Houston region skew-t estimates. that, by 2040, solar capacity in Texas could increase to 4 GW, making it the largest producer of solar energy nationwide. According to the Texas Comptroller's Office, \"Texas is experiencing a population boom, adding nearly 4 million residents over the past decade\". The EIA estimates that the influx of new companies and migrating populations will increase the demand for electricity by at least 25% by 2035.