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This article presents a monitoring strategy based on multilayer principal component analysis (PCA) to detect and diagnose power system disturbances in large amounts of data collected by intelligent electronic devices in low voltage smart grids. The PCA models are built on multiple sliding windows, sized (in terms of length and sampling time) according to the type of phenomena to detect. Abnormalities are detected with use of two complementary statistical indexes, then diagnosed by computing the individual contributions of each monitored variable to the constraint violation of those statistics. As a result, its implementation enables an automatic analysis of multiple phenomena of interest in parallel over time using distinct electrical quantities. Furthermore, the method is demonstrated within the RESOLVD project with data from the OpenLV project containing measurements of active and reactive power gathered at different low voltage distribution substations ; This work has been supported by the European Union's Horizon 2020 research and innovation framework under the auspices of the project Renewable penetration levered by Efficient Low Voltage Distribution grids, grant agreement number 773715, and University of Girona scholarship

Comunicació de congrés presentada a: The Eleventh Conference on Innovative Smart Grid Technologies (ISGT 2020), sponsored by the IEEE Power & Energy Society (PES), will be held February 17-20, 2020 at the Grand Hyatt Washington, Washington D.C. ; This paper presents a new strategy based on multivariate statistical analysis for fault location and classification in power distribution networks with distributed energy resources, variable loads, and switches enabling grid reconfiguration. The statistical method relies on impedance measurements acquired at the substation buses to build a data-driven model of the network operating conditions with dimensionality reduction, and considers a few reference scenarios representing standard operating conditions and short-circuit operation to perform fault location and classification with use of similarity criteria in the principal component subspace. Moreover, this paper includes a case study with a real-based low voltage power distribution network to test and validate the methodology ; This research was supported by the European Union's Horizon 2020 research and innovation programme, call LCE-01-2016-2017, under the auspices of the project Renewable penetration levered by Efficient Low Voltage Distribution grids, grant agreement number 773715, and University of Girona scholarship

Comunicació de congrés presentada a: 2020 IEEE Power and Energy Society General Meeting, Montreal, QC, Canada, 2020, 3-6 August. https://pes-gm.org/2020/ ; Principal component analysis (PCA) is a dimensionality reduction technique often applied to process and detect events in large amounts of data collected by phasor measurement units (PMU) at transmission and distribution level. This article considers five different approaches to select an appropriate number of principal components, builds the statistical model of the PMU data online over a sliding window of 10 seconds and 1 minute, and evaluates the computation times and the accuracy of correct event detections with use of two statistical tests in a 1−hour data file from the UT-Austin Independent Texas Synchrophasor Network with phasor quantities collected at different PMU substations ; This research was supported by the European Union's Horizon 2020 research and innovation programme, call LCE- 01-2016-2017, under the auspices of the project "Renewable penetration levered by Efficient Low Voltage Distribution grids", grant agreement number 773715, and University of Girona scholarship.

Comunicació de congrés presentada a: IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE) (12-14 agost 2020: Oshawa, ON, Canada) ; This paper presents a new strategy to support fault location in power distribution networks based on arbitrary similarity criteria in the principal component subspace. Input data consist of impedance measurements at the secondary transformer of one or more substation buses, which are used to build datadriven models of the grid operating conditions. The statistical models of actual operating conditions are further compared with a few reference scenarios to determine the network configuration and the type and location of the fault based on arbitrary features which minimize the variability of the data. Furthermore, this paper includes a case study with a real-based low voltage power distribution network to test the method under different faults ; This research was supported by the European Union's Horizon 2020 research and innovation programme, call LCE-01-2016-2017, under the auspices of the project Renewable penetration levered by Efficient Low Voltage Distribution grids, grant agreement number 773715, and University of Girona scholarship

Comunicació de congrés presentada a: 2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), organitzat per l'IEEE Power & Energy Society (PES) i la Delft University of Technology (Països Baixos), del 26 a 28 d'octubre de 2020. https://ieee-isgt-europe.org/ ; This article compares distinct signal-based and knowledge-based approaches often applied to process and detect events in vast amounts of data collected by phasor measurement units (PMU). The computation times and the accuracy of correct event detections are tested and evaluated in a 1-hour data file from the UT-Austin Independent Texas Synchrophasor Network with phasor quantities plus an additive noise gathered at different PMU substations. A sliding time window is considered to build a representative model of the system operating conditions on the fly and search for power system phenomena as soon as new data are available ; This research was supported by the European Union's Horizon 2020 research and innovation programme, call LCE-01-2016-2017, under the auspices of the project "Renewable penetration levered by Efficient Low Voltage Distribution grids", grant agreement number 773715, and University of Girona scholarship

Synergies between multiple energy vectors can support decarbonization of local energy islands and, at the same time, relieve stress from the electricity grid. Multi-vector energy systems offer flexibility to integrate variable and economic local energy generation. However, the implementation and operation of multi-vector energy systems face technical, societal and business-related challenges. This paper identifies these challenges and proposes different tools to tackle them. These tools are to be developed in the E-LAND project funded by European Union under the H2020 scheme. Special focus of the paper is on developing a method for optimal planning and operation of multi-vector grid considering robustness. Another novelty of technical tool lies in making scheduling decisions on long-term and short-term storage considering demand response as flexibility resource. By combining technological, societal and business tools, the project expects to create multi-vector energy systems accepted by citizens and with viable business model ; E-LAND project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824388

The paper describes an ongoing work to embed several services in a Smart City architecture with the aim of achieving a sustainable city. In particular, the main goal is to identify services required in such framework to define the requirements and features of a reference architecture to support the data-driven methods for energy efficiency monitoring or load prediction. With this object in mind, a use case of short-term load forecasting in non-residential buildings in the University of Girona is provided, in order to practically explain the services embedded in the described general layers architecture. In the work, classic data-driven models for load forecasting in buildings are used as an example ; This research project has been partially funded through BR-UdGScholarship of the University of Girona granted to Joaquim MassanaRaurich. Work developed with the support of the research groupSITES awarded with distinction by the Generalitat de Catalunya(SGR 2014–2016), the MESC project funded by the Spanish MINECO (Ref. DPI2013-47450-C2-1-R) and the European Union's Horizon2020 Research and Innovation Programme under grant agreementNo 680708

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)

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)

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)

The paper presents a set of prototype smart grid technologies and services and validates the economic viability of the proposed solution using cost–benefit analysis (CBA). The study considered the EU-funded project called RESOLVD and implemented the technologies and services in a real-life pilot. The paper focuses on the analysis of technological solutions which enhance the operational efficiency and the hosting capacity of low-voltage electricity distribution grids. The solutions provided better integration of a hybrid battery storage system, with the grid interfacing power electronics, smart gateways for the interconnection of assets at the grid edge, and sensors enhancing infrastructure observability and control. The result from the CBA indicates the economic viability of the project, high scalability, and replicability. The economic benefits were realized with the breakeven value of eight secondary substations (SS) and 16 feeders. The scenario test on the DSO's willingness to pay for the software as a service (SaaS) revealed that the payback period can further be reduced by almost half with a higher internal rate of return (IRR) and net present value (NPV). Both the CBA and scenario tests showed RESOLVD solution can become more economically viable when deployed in largescale. Moreover, the CBA results provide evidence to the energy policy by allowing DSOs to consider both CAPEX and OPEX for better investment decisions. Further, the paper proposes an alternative business approach that shifts from grid reinforcement to service provision. The paper also discusses the research implications on energy policy and business ; The RESOLVD project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 773715