• Energy Research

This paper explores the viability of electric vehicle charging point operators to act as flexibility service providers in local flexibility markets. The work focuses on the requirements for operating in local intra-day markets and specifically in solving grid congestion at the distribution level. The explored approach assumes an alternative to bilateral agreements constrained to the capacity of the charging point operator to forecast the electric vehicle demand and flexibility effectively.The current paper analyses the flexibility capacity and proposes a methodology to address the re-dispatch process within the GOPACS (The Netherlands) context.The flexibility estimation methodology comprises two forecasting steps: forecasting the aggregated flexibility capacity and forecasting electric vehicles flexibility. A detailed case study presents data from the real electric vehicle sessions in Amsterdam City. The experimental results validate the effectiveness of the proposed methodology, establishing a robust basis for further research.

The electric grid is evolving. Smart grids and demand response systems will increase the performance of the grid in terms of cost efficiency, resilience and safety. Accurate load forecasting is an important issue in the daily operation and control of a power system. A suitable short term load forecasting will enable a utility provider to plan the resources and also to take control measures to balance the supply and demand of electricity. The aim of this paper is to create a method to forecast the electric load in a non-residential building. Another goal is to analyse what kind of data, as weather, indoor ambient, calendar and building occupancy, is the most relevant in building load forecasting. A simple method, tested with three different models, such as MLR, MLP and SVR, is proposed. The results, from a real case study in the University of Girona, show that the proposed forecast method has high accuracy and low computational cost This research has been partially supported by the Spanish Government project MESC (Ref. DPI2013-47450-C2-1-R). Also we would like to thank the Department of Physics and acknowledge the technical assistance and maintenance service of the UdG (SOTIM) which provided the weather and consumption data respectively. The authors belong to the ‘Smart IT Engineering and Solutions’ accredited research group (Generalitat de Catalunya, 2014 SGR 1052)

Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the '70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.

Heating, ventilation, and air conditioning (HVAC) systems account for up to 40% of the total energy consumption in buildings. Improving the modeling of HVAC components is necessary to optimize energy efficiency, maintain indoor thermal comfort, and reduce their carbon footprint. This work addresses the lack of a general methodology for data preprocessing by introducing a novel approach for feature extraction and feature selection based on physical equations and expert knowledge that can be applied to any data-driven model. The proposed framework enables the forecasting of indoor temperatures and the energy consumption of individual HVAC components. The methodology is validated with real-world data from a system involving a fan coil unit and a thermal inertia deposit powered by geothermal energy, achieving a coefficient of determination (R2) of 0.98 and mean absolute percentage error (MAPE) of 0.44%.

A complete methodology for energy building monitoring based on Principal Component Analysis (PCA) is proposed. The method extends the Unfolding or Multiway Principal Component Analysis (MPCA) used in statistical batch process control in terms of building and neighbourhood monitoring. Relationships between energy consumption and independent variables such as weather, occupancy or any other variables that are significant for monitoring can be gathered in a model using the proposed methodology. Historic data are used to obtain a reference model that will be used for monitoring. Two unfolding strategies are proposed (time-wise and entity-based) offering complementary views of the building or of the community under consideration. The first, time-wise unfolding, is suitable for detecting behavioural changes over time, whereas entity-wise unfolding allows the identification of entities, e.g. dwellings in a building, that behave substantially differently from others over a period of time. Two simple statistics, T2 and SPE, are used to define two monitoring charts capable of detecting abnormal behaviours and, furthermore, the isolation of variables that mainly explain such a situation. The paper presents the theoretical background, followed by the methodological principles. The results are illustrated by a case study This work has been developed within the project Plataforma para la monitorización y evaluación de la eficiencia de los sistemas de distribución en Smart Cities, ref. DPI2013-47450-C2-1-R and project ACCUS (Adaptive Cooperative Control in Urban (sub) Systems., ART-010000-2013-2 -333020-1), funded by the Spanish Ministry of Industry, Energy and Tourism and by the JTI ARTEMIS Joint Undertaking of the European Commission. Appreciation is given for the data provided by the Patronat de l’habitatge de Barcelona with the collaboration of AITEL. Data fromMeteocat were also used