Et al. (2019), [69]. Data/Period GME/2005013 AEMO/ 2011013 EEX/2008016 NEM/2010018 Country Italy Australia Germany Australia Process (s) Time series (OLS) analysis Time series regression analysis Time series regression analysis ARDL model Econometric analysis tactics (a supply/demand evaluation for electricity markets) Findings The merit-order effect for wind power was identified. The merit-order effect for wind energy was found. The merit-order impact for wind energy was discovered. The merit-order impact for wind power was located. The merit-order impact for wind energy was located and wind generation had an effect around 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 Country Italy System (s) Time series regression analysis Panel information evaluation (fixed impact regression) VAR framework (Granger causality tests and impulse response functions) A many linear regression model Quantile regression model Numerous linear regression models (Basic cost modeling) Quantile Regression Averaging and Quantile Regression Machine VAR model Findings It was found that wind generation energy induced higher imbalance values. It was found that there have been dampening effects of wind energy on MCPs, however this effect began to lower soon after 2013. It was discovered that intraday rates responded to wind power forecast errors. It was shown that the 15 min scale became widespread in intraday Phenyl acetate Purity trading and helped drastically to minimize imbalances. It was located that wind power TFV-DP Technical Information generations had a damaging effect around the MCPs. It was shown that the made use of models effectively explained the spot price variance. It was shown that QRM was both a lot more effective and had a lot more correct distributional predictions. It was discovered that wind forecast errors had no influence on value spreads in areas having a huge level of wind energy generation. Wind generation had a damaging effect on electrical energy rates. It was discovered that trading efficiency may be enhanced by DAM forecasts. It was identified that making use of the law of supply/demand curve yields realistic patterns for electrical energy prices and leads to promising results. Much more highly effective variables identified and suggestions have been supplied for superior 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: Market clearing prices 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.
