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Decarbonisation policies aim at reducing fossil fuel based generation in favour of cleaner renewable energy sources. Changes in the generation mix to supply future electricity demand will require tools capable to emulate the bidding behaviour of new generation plants. Price forecasting tools lacking this feature and only based on historical data time series might soon become not satisfactory for this scope. This paper presents a methodology that, by considering hourly electricity generation offers (price, volumes) datasets, allows simulating future electricity wholesale's prices. This is done by taking into account new generation units and the dismissing of old (coal-based) units according to the demand and generation forecasts in the European Ten Year Network Development Plan (TYNDP) 2030 scenarios. Machine learning, clustering and distribution sampling techniques are used in this work to finally estimate prices distribution in 2030 in the biggest bidding zone of the Italian market. The results suggest that the prices obtained in the different scenarios do converge to those estimated by the TYNDP. The approach used bypasses the need to have access to all the transactions of a given market. Probability distributions are in fact enough in the proposed methodology to achieve similar results to those based on full knowledge of transaction datasets.
Marco G. Flammini; Giuseppe Prettico; Andrea Mazza; Gianfranco Chicco. Reducing fossil fuel-based generation: Impact on wholesale electricity market prices in the North-Italy bidding zone. Electric Power Systems Research 2021, 194, 107095 .
AMA StyleMarco G. Flammini, Giuseppe Prettico, Andrea Mazza, Gianfranco Chicco. Reducing fossil fuel-based generation: Impact on wholesale electricity market prices in the North-Italy bidding zone. Electric Power Systems Research. 2021; 194 ():107095.
Chicago/Turabian StyleMarco G. Flammini; Giuseppe Prettico; Andrea Mazza; Gianfranco Chicco. 2021. "Reducing fossil fuel-based generation: Impact on wholesale electricity market prices in the North-Italy bidding zone." Electric Power Systems Research 194, no. : 107095.
Electrical distribution networks are facing an energy transition which entails an increase of decentralised renewable energy sources and electric vehicles. The resulting temporal and spatial uncertainty in the generation/load patterns challenges the operations of an infrastructure not designed for such a transition. In this situation, Optimal Power Flow methods can play a key role in identifying system weak points and supporting efficient management of the electrical networks, including the distribution level. In this work, to support distribution system operators’ decision-making process, we aim at attaining a quasi-optimal solution in the shortest time possible in an electrical network experiencing a large growth of distributed energy sources. We propose an optimisation method based on a modified version of a genetic algorithm and the Python pandapower package. The method is tested on a model of a real urban meshed network of a large Czech city. The optimisation method minimises the total operating costs of the distribution network by controlling selected network components and parameters, namely the transformer tap changers and the active power demand at consumption nodes. The results of our method are compared with the exact solution showing that a close-to-optimal solution of the observed problem can be reached in a relatively short time.
Ladislav Foltyn; Jan Vysocký; Giuseppe Prettico; Michal Běloch; Pavel Praks; Gianluca Fulli. OPF solution for a real Czech urban meshed distribution network using a genetic algorithm. Sustainable Energy, Grids and Networks 2021, 26, 100437 .
AMA StyleLadislav Foltyn, Jan Vysocký, Giuseppe Prettico, Michal Běloch, Pavel Praks, Gianluca Fulli. OPF solution for a real Czech urban meshed distribution network using a genetic algorithm. Sustainable Energy, Grids and Networks. 2021; 26 ():100437.
Chicago/Turabian StyleLadislav Foltyn; Jan Vysocký; Giuseppe Prettico; Michal Běloch; Pavel Praks; Gianluca Fulli. 2021. "OPF solution for a real Czech urban meshed distribution network using a genetic algorithm." Sustainable Energy, Grids and Networks 26, no. : 100437.
Decarbonisation policies have recently seen an uncontrolled increase in local electricity production from renewable energy sources (RES) at distribution level. As a consequence, bidirectional power flows might cause high voltage/ medium voltage (HV/MV) transformers to overload. Additionally, not-well-planned installation of electric vehicle (EV) charging stations could provoke voltage deviations and cables overloading during peak times. To ensure secure and reliable distribution network operations, technology integration requires careful analysis which is based on realistic distribution grid models (DGM). Currently, however, only not geo-referenced synthetic grids are available inliterature. This fact unfortunately represents a big limitation. In order to overcome this knowledge gap, we developed a distribution network model (DiNeMo) web-platform aiming at reproducing the DGM of a given area of interest. DiNeMo is based on metrics and indicators collected from 99 unbundled distribution system operators (DSOs) in Europe. In this work we firstly perform a validation exercise on two DGMs of the city of Varaždin in Croatia. To this aim, a set of indicators from the DGMs and from the real networks are compared. The DGMs are later used for a power flow analysis which focuses on voltage fluctuations, line losses, and lines loading considering different levels of EV charging stations penetration.
Mirna Grzanic; Marco Giacomo Flammini; Giuseppe Prettico. Distribution Network Model Platform: A First Case Study. Energies 2019, 12, 4079 .
AMA StyleMirna Grzanic, Marco Giacomo Flammini, Giuseppe Prettico. Distribution Network Model Platform: A First Case Study. Energies. 2019; 12 (21):4079.
Chicago/Turabian StyleMirna Grzanic; Marco Giacomo Flammini; Giuseppe Prettico. 2019. "Distribution Network Model Platform: A First Case Study." Energies 12, no. 21: 4079.
Concerns about climate change, pollution and energy security have prompted policies aiming at replacing fossil fuels (in heating and cooling, and transportation) with electricity, presumably generated from renewable sources. Climate change itself is expected to increase the demand for cooling in buildings, which is generally met with electricity-powered air conditioning. We use hourly electricity demand from a sample of Italian residences over a full year to examine how sensitive residential demand is to temperature. Our regression model includes a rich set of household-by-time fixed effects to control for dwelling characteristics and equipment, family composition, work and business schedules, demand for lighting, and seasonal habits other than temperature. These allows us to separate the effect of temperature from the demand for lighting and from other seasonal effects that may be correlated with temperature, but are not temperature. We find that demand stays within a relatively narrow range (and is thus relatively flat) up to temperatures of about 24.4 °C, and increases sharply with temperature thereafter. We find that temperature accounts for a very small share of daily electricity demand. Only on exceptionally hot summer days can temperature account for 12% of hourly electricity use.
Anna Alberini; Giuseppe Prettico; Chang Shen; Jacopo Torriti. Hot weather and residential hourly electricity demand in Italy. Energy 2019, 177, 44 -56.
AMA StyleAnna Alberini, Giuseppe Prettico, Chang Shen, Jacopo Torriti. Hot weather and residential hourly electricity demand in Italy. Energy. 2019; 177 ():44-56.
Chicago/Turabian StyleAnna Alberini; Giuseppe Prettico; Chang Shen; Jacopo Torriti. 2019. "Hot weather and residential hourly electricity demand in Italy." Energy 177, no. : 44-56.
Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO2eq/MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.
Alexandre Lucas; Giuseppe Prettico; Marco Giacomo Flammini; Evangelos Kotsakis; Gianluca Fulli; Marcelo Masera. Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis. Energies 2018, 11, 1869 .
AMA StyleAlexandre Lucas, Giuseppe Prettico, Marco Giacomo Flammini, Evangelos Kotsakis, Gianluca Fulli, Marcelo Masera. Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis. Energies. 2018; 11 (7):1869.
Chicago/Turabian StyleAlexandre Lucas; Giuseppe Prettico; Marco Giacomo Flammini; Evangelos Kotsakis; Gianluca Fulli; Marcelo Masera. 2018. "Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis." Energies 11, no. 7: 1869.
Current decarbonisation goals have, in recent years, led to a tremendous increase in electricity production generated from intermittent Renewable Energy Sources. Despite their contribution to reducing society’s carbon dioxide (CO2) emissions they have been responsible for numerous challenges that the current electricity grid has to cope with. Flexibility has become a key mechanism to help in mitigating them. Real-time informed consumers can offer the needed flexibility through modifying their behaviour or by engaging with Demand Side Management (DSM) programs. The latter requires the intervention of several actors and levels of communication management which makes this task difficult from an implementation perspective. With this aim we built and tested a small scale system in our lab which represents a real end-to-end system from the consumer to the energy provider. We programmed the system according to the Object Identification System (OBIS) specification to obtain consumers’ consumption through smart meters with high frequency (one minute). This allows remote control of their appliances in order to reduce the total neighbourhood consumption during critical time periods of the day (peak time). These results and the realisation of a realistic end-to-end system open the way to more complex tests and particularly to the possibility of benchmarking them with other lab tests.
Nikoleta Andreadou; Yannis Soupionis; Fausto Bonavitacola; Giuseppe Prettico. A DSM Test Case Applied on an End-to-End System, from Consumer to Energy Provider. Sustainability 2018, 10, 935 .
AMA StyleNikoleta Andreadou, Yannis Soupionis, Fausto Bonavitacola, Giuseppe Prettico. A DSM Test Case Applied on an End-to-End System, from Consumer to Energy Provider. Sustainability. 2018; 10 (4):935.
Chicago/Turabian StyleNikoleta Andreadou; Yannis Soupionis; Fausto Bonavitacola; Giuseppe Prettico. 2018. "A DSM Test Case Applied on an End-to-End System, from Consumer to Energy Provider." Sustainability 10, no. 4: 935.