We present a game-theoretical framework that merges electricity market models and model predictive control concepts. We demonstrate that the framework can be used to systematically analyze the effects of ramp constraints, initial conditions, dynamic disturbances, forecast horizon, and bidding frequency on the price signals. We illustrate the capabilities of the framework using a numerical case ...

In liberalized electricity markets, Generation Companies must build an hourly bid that is sent to the market operator. The price at which the energy will be paid is unknown during the bidding process and has to be forecast. In this work we apply forecasting factor models to this framework and study its suitability.

The worldwide electric power industry has seen many changes over the last 20 years. During this period many regulated or state-owned monopoly markets have been deregulated. In an electricity market, electricity price is decided based on demand and supply bids from the market participants; therefore, the importance of ShortTerm Load Forecasting (STLF) has been rising in these markets [1]. Load f...

Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied different degrees of success. It is great interest to industry sector, becoming must-have tool for risk management. Most forecast electricity itself; this paper gives new perspective by trying dynamics behind price: supply demand curves originating from auction. Given complexity...

Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase of forecast accuracy. In this paper, different factors were studied in comparison to the p...

This study presents the implication of Bayesian technique on simple statistical and econometric forecasting models to improve the forecast performances of the models. We consider a nonlinear, non-stationary time series of household electricity demand to demonstrate the Bayesian implication to statistical techniques. In this forecasting process, the electricity demand is considered a function of...

The objective of this study was to assess the benefits of introducing a demand side management optimisation controller to a cold thermal storage ice bank. This controller consisted of an ice bank model, an air temperature forecast model and an optimisation algorithm. The financial and grid utilisation benefits produced by implementation of this controller over the current state of the art in ic...

This work examines recent publications in forecasting in various fields, these include: wind power forecasting; electricity load forecasting; crude oil price forecasting; gold price forecasting energy price forecasting etc. In this review, categorization of the processes involve in forecasting are divided into four major steps namely: input features selection; data pre-processing; forecast mode...

This study presents a genetic algorithm (GA) with variable parameters to forecast electricity demand in agricultural, low energy consuming and energy intensive sectors using stochastic procedures. The economic indicators used in this paper are price, value added, number of customers and consumption in the last periods for agricultural and low energy consuming sectors and price, value added, num...