The uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately suppo...

Load forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This study shows Electricity Load Forecasting modeling based on Framelet Neural Network Technique (FNN) for Baghdad City. Framelet technique is implemented to the time series data, decomposing the data into number of Framelet coefficient signal...

We model the supply and demand curves of electricity day-ahead auctions in a parsimonious way by building an appropriate algorithm to present information about prices with far fewer parameters than existing algorithm. represent each curve using mesh-free interpolation techniques based on radial basis function approximation. describe results this method for IPEX spot price Italy then use these r...

Energy plays a significant role in today's developing societies. The role of energy demands to make decisions and policy with regard to its production, distribution, and supply. The vital importance of energy, especially fossil fuels, is a factor affecting agricultural production. This factor has a great influence on the production of agricultural products in Iran. The forecast of the con...

In this paper the problem of modeling electricity price dynamics in deregulated energy markets is studied. Since the nature of customers' energy consumption, market participants' strategic behavior and the power system reliability indices are stochastic processes, the electricity price model will be stochastic as well. In our analysis the price of electricity is modeled based on the customers' ...

High electricity demand peaks and uncertain supply from renewable energy sources have a significant impact on the electricity price and the network capacity. One mechanism proposed to tackle this issue is the use of real-time pricing (RTP) at the end customer level. Here electricity retail prices are set in real-time in response to varying supply-demand conditions in a way that reduces peak dem...

In this paper, for efficient energy consumption through the residential demand response in the smart grid, an optimization algorithm, which can provide a schedule plan for the home appliance usages, is proposed. In order to minimize the average electricity price based on the time-varying electricity price in conjunction with the peak hourly load, which decides the capacity of the electric suppl...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...

Wind Power Producers (WPPs) seek to maximize profit and minimize the imbalance costs when bidding into the day-ahead market, but uncertainties in the hourly available wind and forecasting errors make the bidding risky. This paper assumes that hourly wind power output given by the forecast follows a normal distribution, and proposes three different bidding strategies, i.e., the expected profit-m...

This paper proposed a novel model for short term load forecast (STLF) in the electricity market. The prior electricity demand data are treated as time series. The model is composed of several neural networks whose data are processed using a wavelet technique. The model is created in the form of a simulation program written with MATLAB. The load data are treated as time series data. They are dec...