Electricity rates differ in each country, some will pay a very less amount for electricity bills others will pay a very large amount. For that, some countries don’t have power shortages but some will have more power shortages most of the village areas don’t have power during the nighttime, they live life without power itself and they practice it till life ends. This issue was more affected in the USA. So some researchers formed a committee to solve this issue. For that, they create Hamlin Energy Rates price modeling. Because in those areas only large number power plants available. In that model, it has two important sections such as literature review and quantile regression.
This leisure review regards the modeling of electricity rates in different markets. Most of the efforts of preview work go through the future price of elasticity rates. Forecasting electricity prices are used in several methods that were driven by Bunn and karakatsani. Transfer function and a dynamic regression model are developed for accurate price forecast to California and Spanish electricity market with the help of Nogales researcher, etc. Time series models are developed by Torro along with the Arimax model to show weekly prices at the Nord pool market. Some researchers limit the forecasting model to autoregressive effects and some to variables. Some of them argue with those models cannot have complicated markets. For these problems in the British market, they applied a regime-switching model and time-varying parameter regression model, which consists of large explanatory variables. It concludes with the best predictive performance obtained from models involving nonlinearity, market fundamentals, and time-varying coefficients. Researcher Chen appreciates the karakatsani and Bunn for the non-linear relationship between fundamental drivers in the British market and electricity rates. A structural finite mixture regression model was developed by Chen and its performance outperforms linear regression models and regime-switching models. The result, it shows that prices in the different trading periods within a day by several factors. These results are confirmed by Chen and Bunn by using the logistic smooth transition regression model. Several model techniques are used to capture and model the distribution of high price behavior. Dinner researchers use dynamic, multi-factor, quantile regression formulation and it shows how the price was very high across quantiles. The elasticity of coal, gas, and carbon prices has no specific patterns in quantiles but these are more influenced by price distribution. Thomas and other researchers create an autoregressive model to capture the effect of every spike individually while controlling the seasonality in spot price in the market. Demand and supply information are more adequate in negative prices. Christensen extends the research on AR models by simply looking at the prediction of spikes used in the autoregressive conditional hazard model. The logit model was used to get the same result so it was a commonly used model. It also focuses on the short-term forecast of door occurrence. The dynamic binary response model has a superior fit on the overall market and it was replaced by logistic function by asymmetric link function that leads to significant improvements. Some researchers extend the ACH model used in incorporating past price information by improving the model performance. Bunn applies some statistical models to go through the relationship between electricity rates and fundamental drivers in the UK market which consists of many pieces of the puzzle to understand the high price.