Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

María Carmen Ruiz-Abellón, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Alberto Falces, Ana García-Garre and Antonio Gabaldón
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María Carmen Ruiz-Abellón: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30203 Cartagena, Spain
Luis Alfredo Fernández-Jiménez: Department of Electrical Engineering, Universidad de La Rioja, 26004 Logroño, La Rioja, Spain
Antonio Guillamón: Department of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, 30203 Cartagena, Spain
Alberto Falces: Department of Electrical Engineering, Universidad de La Rioja, 26004 Logroño, La Rioja, Spain
Ana García-Garre: Electrical Engineering Area, Universidad Politécnica de Cartagena, 30203 Cartagena, Spain
Antonio Gabaldón: Electrical Engineering Area, Universidad Politécnica de Cartagena, 30203 Cartagena, Spain

Energies, 2019, vol. 13, issue 1, 1-31

Abstract: The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

Keywords: short-term load forecasting; demand response; distributed energy resources; prosumers (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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