Et al. (2019), [69]. Data/Period GME/2005013 AEMO/ 2011013 EEX/2008016 NEM/2010018 Nation Italy Australia Germany Australia System (s) Time series (OLS) evaluation Time series regression Pregnanediol site analysis Time series regression evaluation ARDL model Econometric analysis methods (a supply/demand evaluation for electricity markets) Findings The merit-order impact for wind power was identified. The merit-order effect for wind energy was located. The merit-order impact for wind power was located. The merit-order effect for wind energy was discovered. The merit-order effect for wind power was discovered and wind generation had an influence around the MCPs.Forrest and MacGill (2013), [70].AEMO and NEM /2009AustraliaEnergies 2021, 14,8 ofTable 2. 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 Country Italy System (s) Time series regression evaluation Panel data analysis (fixed impact regression) VAR framework (Granger causality tests and impulse response functions) A numerous linear regression model Quantile regression model Several linear regression models (Fundamental cost modeling) Quantile Regression Averaging and Quantile Regression Machine VAR model Findings It was located that wind generation power induced high imbalance values. It was found that there were dampening effects of wind energy on MCPs, on the other hand this impact started to lower immediately after 2013. It was located that intraday prices responded to wind power forecast errors. It was shown that the 15 min scale became popular in intraday trading and helped considerably to lower imbalances. It was Dodecyl gallate custom synthesis identified that wind energy generations had a adverse impact on the MCPs. It was shown that the made use of models well explained the spot cost variance. It was shown that QRM was both extra efficient and had far more correct distributional predictions. It was found that wind forecast errors had no impact on value spreads in locations having a big level of wind energy generation. Wind generation had a unfavorable impact on electricity rates. It was discovered that trading efficiency could be enhanced by DAM forecasts. It was discovered that using the law of supply/demand curve yields realistic patterns for electricity costs and leads to promising final results. Extra potent variables identified and recommendations had been supplied for 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 costs NEM: The Australian National Electricity Market’sZiel and Weron, (2018b), [87].EPEX, Nord Pool, BELPEX/ 2011European CountriesAEMO: Australia Energy Industry Operator ARDL: Autoregressive distributed la.
