Et al. (2019), [69]. Data/Period GME/2005013 AEMO/ 2011013 EEX/2008016 NEM/2010018 Nation Italy Australia Germany Australia

Et al. (2019), [69]. Data/Period GME/2005013 AEMO/ 2011013 EEX/2008016 NEM/2010018 Nation Italy Australia Germany Australia Process (s) Time series (OLS) analysis Time series regression analysis Time series regression evaluation ARDL model Econometric analysis tactics (a supply/demand evaluation for electricity Piperonylic acid MedChemExpress markets) Findings The merit-order effect for wind power was found. The merit-order effect for wind power was discovered. The merit-order impact for wind power was located. The merit-order effect for wind energy was discovered. The merit-order impact for wind power was found and wind generation had an impact on the MCPs.Forrest and MacGill (2013), [70].AEMO and NEM /2009AustraliaEnergies 2021, 14,8 ofTable two. Cont. Author (s) Gianfreda et al. (2016), [31]. Data/Period ENTSO-E/ 2012014 ENTSOE/2010016 Nord Pool FTP server and ENTSOE/2015018 ENTSO-E and TSO/2012017 EPEX and ENTSO-E/ 2015018 ENTSO-E, EEX, EPEX/2012013 Nation Italy Method (s) Time series regression analysis Panel data evaluation (fixed impact regression) VAR framework (Granger causality tests and impulse response functions) A various linear regression model Quantile regression model Several linear regression models (Fundamental price modeling) Quantile Regression Averaging and Quantile Regression Machine VAR model Findings It was found that wind generation energy induced higher imbalance values. It was identified that there were dampening effects of wind power on MCPs, however this effect started to lower just after 2013. It was found that intraday costs responded to wind energy forecast errors. It was shown that the 15 min scale became popular in intraday trading and helped significantly to reduce imbalances. It was found that wind power generations had a unfavorable impact around the MCPs. It was shown that the utilised models well explained the spot price tag variance. It was shown that QRM was both much more efficient and had much more correct distributional predictions. It was found that wind forecast errors had no impact on price tag spreads in areas having a Thiacloprid site massive volume of wind power generation. Wind generation had a damaging effect on electrical energy prices. It was discovered that trading efficiency could be enhanced by DAM forecasts. It was found that applying the law of supply/demand curve yields realistic patterns for electricity prices and results in promising outcomes. Far more strong variables identified and recommendations have been provided for much better performing models. PJM: The Pennsylvania ew Jersey aryland Interconnection OLS: Ordinary least squares QRM: Quantile regression machine VAR: The vector autoregressiveG tler et al. (2018), [88].GermanyHu et al. (2018), [42].SwedenKoch and Hirth, (2019), [32].GermanyMaciejowska (2020), [71].GermanyPape et al. (2016), [77].Germany Denmark, Finland, Norway, and Sweden Denmark, Sweden, and Finland US (California) US (California)Serafin et al. (2019), [89].Nord Pool, PJM/2013Spodniak et al. (2021), [73].ENTSO-E, Nord Pool/2015017 LCG Consulting, OASIS/ 2013016 CAISO/ 2012Westgaard et al. (2021), [72].Quantile regressionWoo et al. (2016), [66].OLS RegressionZiel and Steinert, (2018a), [90].EPEX/2012Germany and AustriaTime series models (supply/demand curves) Multivariate and univariate models. EPEX: The European Energy Exchange GME: Gestore dei Mercati Energetici MCPs: Industry clearing rates NEM: The Australian National Electricity Market’sZiel and Weron, (2018b), [87].EPEX, Nord Pool, BELPEX/ 2011European CountriesAEMO: Australia Power Marketplace Operator ARDL: Autoregressive distributed la.