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Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.
Alessandro Bosisio; Matteo Moncecchi; Andrea Morotti; Marco Merlo. Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience. Energies 2021, 14, 4133 .
AMA StyleAlessandro Bosisio, Matteo Moncecchi, Andrea Morotti, Marco Merlo. Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience. Energies. 2021; 14 (14):4133.
Chicago/Turabian StyleAlessandro Bosisio; Matteo Moncecchi; Andrea Morotti; Marco Merlo. 2021. "Machine Learning and GIS Approach for Electrical Load Assessment to Increase Distribution Networks Resilience." Energies 14, no. 14: 4133.
Energy communities (EC) are expected to have a pivotal role to reach European decarbonization targets. One of the key aspects is the regulatory framework adopted by each Member State to properly manage such new customers’ aggregation. The paper firstly provides an updated overview of the EC regulation, focusing on the current Italian legislation. Next, a novel methodology for the design and management of energy community initiatives is proposed. The procedure firstly solves a design and operation optimization problem to calculate the best size of energy assets (boiler, heat pump, photovoltaic, thermal storage) to be installed. Second, a Shapley value-based approach is exploited to distribute a part of the community’s incomes to members, based on their contribution to the overall welfare. Results demonstrate that the adopted methodology is effective in ensuring a proper cash flow for the community, while pushing its members towards energy efficient behaviors.
Matteo Zatti; Matteo Moncecchi; Marco Gabba; Alberto Chiesa; Filippo Bovera; Marco Merlo. Energy Communities Design Optimization in the Italian Framework. Applied Sciences 2021, 11, 5218 .
AMA StyleMatteo Zatti, Matteo Moncecchi, Marco Gabba, Alberto Chiesa, Filippo Bovera, Marco Merlo. Energy Communities Design Optimization in the Italian Framework. Applied Sciences. 2021; 11 (11):5218.
Chicago/Turabian StyleMatteo Zatti; Matteo Moncecchi; Marco Gabba; Alberto Chiesa; Filippo Bovera; Marco Merlo. 2021. "Energy Communities Design Optimization in the Italian Framework." Applied Sciences 11, no. 11: 5218.
With the Clean Energy Package, the European Union introduced the concept of Renewable Energy Communities: groups of citizens, small and medium enterprises and local authorities that decide to join forces to equip themselves with systems to produce and share energy from renewable energy sources. The Italian legislation recently started an experimental phase in which renewable energy communities receive an incentivising tariff for the energy produced and shared within the community. This paper faces the problem of creating a new renewable energy community in two steps. First, a mathematical model of the energy flows among the members of the community is characterised according to the Italian schema. This model is used to find the optimal portfolio for the energy community, given energy requests and local source availability. Secondly, the Shapley value, a particular solution of cooperative games known to be the most fair method to allocate costs and profits of shared infrastructures, is proposed to distribute benefits among community members. The methodology has been applied to a case study based on a real low voltage network, and the economics for consumers and producers in participating to the project have been evaluated. The proposed solution, simulated adopting real economic parameters defined in the Italian regulatory framework, results to be economically viable from the point of view of the investors with a profitability index of 1.36 and, at the same time, aligned with the social purposes of the energy communities.
Matteo Moncecchi; Stefano Meneghello; Marco Merlo. A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities. Applied Sciences 2020, 10, 8166 .
AMA StyleMatteo Moncecchi, Stefano Meneghello, Marco Merlo. A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities. Applied Sciences. 2020; 10 (22):8166.
Chicago/Turabian StyleMatteo Moncecchi; Stefano Meneghello; Marco Merlo. 2020. "A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities." Applied Sciences 10, no. 22: 8166.
The goal of the paper is to develop an online forecasting procedure to be adopted within the H2020 InteGRIDy project, where the main objective is to use the photovoltaic (PV) forecast for optimizing the configuration of a distribution network (DN). Real-time measurements are obtained and saved for nine photovoltaic plants in a database, together with numerical weather predictions supplied from a commercial weather forecasting service. Adopting several error metrics as a performance index, as well as a historical data set for one of the plants on the DN, a preliminary analysis is performed investigating multiple statistical methods, with the objective of finding the most suitable one in terms of accuracy and computational effort. Hourly forecasts are performed each 6 h, for a horizon of 72 h. Having found the random forest method as the most suitable one, further hyper-parameter tuning of the algorithm was performed to improve performance. Optimal results with respect to normalized root mean square error (NRMSE) were found when training the algorithm using solar irradiation and a time vector, with a dataset consisting of 21 days. It was concluded that adding more features does not improve the accuracy when adopting relatively small training sets. Furthermore, the error was not significantly affected by the horizon of the forecast, where the 72-h horizon forecast showed an error increment of slightly above 2% when compared to the 6-h forecast. Thanks to the InteGRIDy project, the proposed algorithms were tested in a large scale real-life pilot, allowing the validation of the mathematical approach, but taking also into account both, problems related to faults in the telecommunication grids, as well as errors in the data exchange and storage procedures. Such an approach is capable of providing a proper quantification of the performances in a real-life scenario.
Aleksandar Dimovski; Matteo Moncecchi; Davide Falabretti; Marco Merlo. PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot. Energies 2020, 13, 5330 .
AMA StyleAleksandar Dimovski, Matteo Moncecchi, Davide Falabretti, Marco Merlo. PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot. Energies. 2020; 13 (20):5330.
Chicago/Turabian StyleAleksandar Dimovski; Matteo Moncecchi; Davide Falabretti; Marco Merlo. 2020. "PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot." Energies 13, no. 20: 5330.
Battery energy storage systems (BESS) are spreading in several applications among transmission and distribution networks. Nevertheless, it is not straightforward to estimate their performances in real life working conditions. This work is aimed at identifying test power profiles for stationary residential storage applications capable of estimating BESS performance. The proposed approach is based on a clustering procedure devoted to group daily power profiles according to their battery efficiency. By performing a k-means clustering on a large dataset of load and generation profiles, four standard charge/discharge profiles have been identified to test BESS’ performances. Different clustering approaches have been considered, each of them splitting the dataset according to different properties of the profiles. A well-performing clustering approach resulted, based on the adoption of reference parameters for the clustering process of the maximum power exchanged by the BESS and the variation of battery energy content. Firstly, the results have been proven through a numerical procedure based on a BESS electrical model and on the definition of a key performance index. Then, an experimental validation has been carried out on a pre-commercial sodium-nickel chloride BESS: this device is available in the IoT lab of Politecnico di Milano within the H2020 InteGRIDy project.
Matteo Moncecchi; Alessandro Borselli; Davide Falabretti; Lorenzo Corghi; Marco Merlo. Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment. Energies 2020, 13, 2719 .
AMA StyleMatteo Moncecchi, Alessandro Borselli, Davide Falabretti, Lorenzo Corghi, Marco Merlo. Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment. Energies. 2020; 13 (11):2719.
Chicago/Turabian StyleMatteo Moncecchi; Alessandro Borselli; Davide Falabretti; Lorenzo Corghi; Marco Merlo. 2020. "Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment." Energies 13, no. 11: 2719.
Off-grid power systems based on photovoltaic and battery energy storage systems are becoming a solution of great interest for rural electrification. The storage system is one of the most crucial components since inappropriate design can affect reliability and final costs. Therefore, it is necessary to adopt reliable models able to realistically reproduce the working condition of the application. In this paper, different models of lithium-ion battery are considered in the design process of a microgrid. Two modeling approaches (analytical and electrical) are developed based on experimental measurements. The derived models have been integrated in a methodology for the robust design of off-grid electric power systems which has been implemented in a MATLAB-based computational tool named Poli.NRG (POLItecnico di Milano—Network Robust desiGn). The procedure has been applied to a real-life case study to compare the different battery energy storage system models and to show how they impact on the microgrid design.
Matteo Moncecchi; Claudio Brivio; Stefano Mandelli; Marco Merlo. Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria. Energies 2020, 13, 2006 .
AMA StyleMatteo Moncecchi, Claudio Brivio, Stefano Mandelli, Marco Merlo. Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria. Energies. 2020; 13 (8):2006.
Chicago/Turabian StyleMatteo Moncecchi; Claudio Brivio; Stefano Mandelli; Marco Merlo. 2020. "Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria." Energies 13, no. 8: 2006.
Matteo Moncecchi; Davide Falabretti; Marco Merlo. Regional energy planning based on distribution grid hosting capacity. AIMS Energy 2019, 7, 264 -284.
AMA StyleMatteo Moncecchi, Davide Falabretti, Marco Merlo. Regional energy planning based on distribution grid hosting capacity. AIMS Energy. 2019; 7 (3):264-284.
Chicago/Turabian StyleMatteo Moncecchi; Davide Falabretti; Marco Merlo. 2019. "Regional energy planning based on distribution grid hosting capacity." AIMS Energy 7, no. 3: 264-284.