Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Profiling of Prosumers for the Needs of Electric Energy Demand Estimation in Microgrids

Karol Fabisz (Poznań University of Economics, Poznań, Poland), Agata Filipowska (Poznań University of Economics, Poznań, Poland) and Tymoteusz Hossa (Poznań University of Economics, Poznań, Poland)
Copyright: © 2015 |Pages: 17
DOI: 10.4018/IJEOE.2015100103
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Abstract

Nowadays, a lot of attention regarding smart grids' development is devoted to delivery of methods for estimation of the energy demand taking into account the behavior of network participants (being single prosumers or groups of prosumers). These methods take an advantage from an analysis of the ex-post data on energy consumption, usually with no additional data about profiles of prosumers. The goal of this paper is to present and validate a method for an energy demand forecasting based on profiling of prosumers that enables estimation of the energy demand for every user stereotype, every hour, every day of the year and even for every device. The paper presents possible scenarios on how the proposed approach can be used for the benefit of the microgrid.
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1. Introduction

The energy market is changing. New entities, such as prosumers and network aggregators appeared and new management, trading, decision support and forecasting tools are being developed. As a result, challenges and questions arise, such as i.e. “How to encourage the prosumers to data sharing?”, “How to make use of the prosumer involvement?”, “How to design and make an advantage of the data provided by the prosumers?” and “How to determine a role of a prosumer?” (Shandurkova et al., 2013; Brendal, 2013; Filipowska et al., 2013).

Currently, energy operators prepare forecasts for large groups of energy consumers based on predefined standard energy profiles or another calculation methods like time-series regression, neuro-fuzzy techniques like ARIMA models, etc. (Ghanbarian, Kavehnia, Askari, Mohammadi, & Keivani, 2007; Churueang & Damrongkulkamjorn, 2005). These methods rely on data on historical energy consumption. In many cases, such solutions work well and give low error forecasts results, because of the statistical law of the large numbers (the larger number of energy consumers, the more aligned the forecast).

However, an increasing number of prosumers is equipped with smart meters and the advanced metering infrastructure (AMI) is expanding (Livgard, 2010). In addition, an exploitation of renewable energy sources like wind or sun (photovoltaic panels) becomes more popular, because of the national energy policies and ecofriendly social trends (Parkinson, Wang & Djilali, 2012; Singh, Alapatt & Poole, 2012). Thus, the emergence of the smart grids solutions and intelligent grids, which have their own generation sources and can work almost independently from the main power grid, is being observed (Taft, 2012). Therefore, there is a need for preparing energy consumption forecast of good quality for small groups of prosumers (sometimes even for individuals), independently from the other groups as such prosumer groups can use the energy from their own generation sources. A single consumer or a small group volatility with regard to energy demand is very hard to predict, so energy analysts have a difficult task trying to deliver forecasts with a low error.

Our research (these results are a subject of other submission) shows, that standard forecasting methods like non-linear regression, seasonal exponential smoothing or local methods need aggregated data from at least 60 prosumers to produce well fitted forecasts. When, we use aggregated data from over 60 prosumers, the mean squared prediction error stabilizes. This means that forecasting for groups smaller than 60 prosumers using standard forecasting methods won’t provide satisfying results (Hossa et al., 2014).

Therefore, there is a need to supplement standard forecasting methods with other approaches. Besides of gathering the historical energy consumption, the data describing each energy prosumer should be possessed. Such datasets will give an opportunity to explain the volatility of energy consumption even for a single prosumer.

The aim of this paper is to present and validate a method for the energy demand estimation in microgrids based on profiling of prosumers (energy producers and consumers) that enables to determine the energy demand for every user stereotype, every hour of the year and for every device. The proposed approach is tailored to the needs of an innovative microgrid management and decision-support system, namely the Future Energy Management System (FEMS).

The paper is structured as follows. Section 2 introduces the notion of the profile, showing the potential exploitation of user data with regard to energy demand estimation. Section 3 describes the method that enables estimation of energy demand based on the prosumers’ data. Section 4 provides a use case-based validation of the proposed method. It includes also possible scenarios on how the suggested approach can be used within a microgrid. Section 5 presents the related work. The paper is summarized in the conclusions section that presents the key points as well as possible directions of our future work.

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