Portfolio Decision of Short-Term Electricity Forecasted Prices through Stochastic Programming

Abstract

Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper describes a new approach to forecast day-ahead electricity market prices, whose methodology is divided into two parts as: (i) forecasting of the electricity price through autoregressive integrated moving average (ARIMA) models; and (ii) construction of a portfolio of ARIMA models per hour using stochastic programming. A stochastic programming model is used to forecast, allowing many input data, where filtering is needed. A case study to evaluate forecasts for the next 24 h and the portfolio generated by way of stochastic programming are presented for a specific day-ahead electricity market. The case study spans four weeks of each one of the years 2014, 2015 and 2016 using a specific pre-treatment of input data of the stochastic programming (SP) model. In addition, the results are discussed, and the conclusions are drawn.

Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence

“…This complies with the economic theory stating that as barriers decline the market becomes more competitive and prices fall. In fact, deregulated electricity markets encourage energy providers to compete between each other and thus set electricity price as an ordinary parameter to characterize an electricity market [1]. Furthermore, there are four factors (RE production, Fossil fuels' price, E-mobility, and EU economy growth) showing a positive influence on future electricity prices development.…”

“…Since then, electricity markets and systems throughout Member States (MS) went towards significant structural changes, incentivized by market competitiveness and by the introduction of new disruptive technologies-Renewable Energy Sources (RES), energy storage, and demand-side management. Liberalization of national electricity markets was followed in parallel by efforts to integrate these markets into a pan-European network, aiming to strengthen the security of supply, improve technical capabilities for network management, and increase pricing transparency and efficiency in both wholesale and retail markets [1][2][3][4][5][6][7][8][9].…”

Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response

“…With responsibility for its own profits and losses, microgrid operators are facing operational risks in the power market. Under many uncertain factors, such as electrical market price [7], renewable energy output [8], and user load [9], how to realize the economic dispatch of microgrids becomes a remarkable issue.…”

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

“…Stochastic optimization, robust optimization, multi-objective optimization and mathematical programming have been widely adopted for research on the wholesale market for market-clearance, and most of this research takes into consideration various types of uncertainties resulting from variable demand or renewable energy supply [29,35,58]. Retailers in the retail electricity market often refer to these optimization methods to guarantee their revenue through deterministic analysis.…”

“…Due to the page limit and many mature approaches that already exist for residential load forecasting [33,34] and portfolio evaluation [35,36], risk management will be the focus of discussion here, along with many recent advances in the research community. In a typical example such as [37], the author utilizes stochastic programming techniques to determine the day-ahead market bidding strategies for retailers with flexible demands to maximize their short-term profit, specifically including a case study based on Sweden's electricity market and consideration of the demand uncertainty of retail customers.…”