Et al. (2019), [69]. Data/Period GME/2005013 AEMO/ 2011013 EEX/2008016 NEM/2010018 Nation Italy Australia Germany Australia Method (s) Time series (OLS) evaluation Time series regression evaluation Time series regression analysis ARDL model Econometric analysis techniques (a supply/demand analysis for electricity markets) Findings The merit-order effect for wind energy was found. The merit-order impact for wind energy was identified. The merit-order impact for wind energy was discovered. The merit-order impact for wind energy was located. The merit-order effect for wind energy was identified and wind generation had an effect on the MCPs.Forrest and MacGill (2013), [70].AEMO and NEM /2009AustraliaEnergies 2021, 14,eight 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 Strategy (s) Time series regression analysis Panel data analysis (fixed effect regression) VAR framework (Granger causality tests and impulse response functions) A a number of linear regression model Quantile regression model Numerous linear regression models (7α-Hydroxy-4-cholesten-3-one Cancer Fundamental price modeling) Quantile Regression Averaging and Quantile Regression Machine VAR model Findings It was located that wind generation power induced high imbalance values. It was discovered that there had been dampening effects of wind power on MCPs, nevertheless this impact started to lower soon after 2013. It was discovered that intraday costs responded to wind energy forecast errors. It was shown that the 15 min scale became typical in intraday trading and helped considerably to lessen imbalances. It was located that wind energy generations had a damaging effect around the MCPs. It was shown that the utilised models properly explained the spot price tag variance. It was shown that QRM was both more efficient and had much more accurate distributional predictions. It was identified that wind forecast errors had no impact on cost spreads in places using a massive Poly(4-vinylphenol) Metabolic Enzyme/Protease amount of wind energy generation. Wind generation had a adverse impact on electrical energy rates. It was discovered that trading efficiency could possibly be enhanced by DAM forecasts. It was located that working with the law of supply/demand curve yields realistic patterns for electrical energy costs and leads to promising benefits. Extra effective variables identified and suggestions were provided for improved 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 place clearing rates NEM: The Australian National Electrical energy Market’sZiel and Weron, (2018b), [87].EPEX, Nord Pool, BELPEX/ 2011European CountriesAEMO: Australia Power Marketplace Operator ARDL: Autoregressive distributed la.
