The entire utility industry and energy systems research communities have recently realized the importance of modeling electric loads in steady-state and dynamic state in the time domain. Predictive analysis of loads, the basis of electrical system planning, is still one of the most difficult aspects to evaluate due to the complexity of developing a suitable dynamic model. The most challenging aspect is to find a suitable way to represent the aggregate behavior of a large number of devices with vastly different sizes and characteristics that, in their totality, constitute a housing unit [1]. In addition, there is variability and uncertainty related to environmental, geographic, economic, political and social conditions that make it extremely challenging to construct a universally valid algorithm. The purpose of this paper is to present a model called Enanched Bottom-Up Model (EBM) that can produce load profiles that are extremely consistent with real ones, minimizing errors and thus allowing for a valuable tool for planning residential electrical systems. This model, as will be shown, is highly dynamic, a feature that allows it to be constantly updated to provide actual results and consider the technological evolution of residential units. To evaluate the effectiveness of the EBM, a case study will then be presented in which a real load profile, realized by direct measurement, is compared with an indirect load profile generated by the algorithm that holds the same conditions, so as to verify how different the realized curves are from each other.

Electrical Load Profiles for Residential Buildings: Enhanced Bottom-Up Model (EBM) / Loggia, Riccardo; Flamini, Alessandro; Massaccesi, Andrea; Moscatiello, Cristina; Galasso, Alessandro; Martirano, Luigi. - (2023), pp. 635-640. (Intervento presentato al convegno 2023 International Conference on Clean Electrical Power, ICCEP 2023 tenutosi a Terrasini, Italy) [10.1109/ICCEP57914.2023.10247473].

Electrical Load Profiles for Residential Buildings: Enhanced Bottom-Up Model (EBM)

Loggia, Riccardo
;
Flamini, Alessandro;Massaccesi, Andrea;Moscatiello, Cristina;Galasso, Alessandro;Martirano, Luigi
2023

Abstract

The entire utility industry and energy systems research communities have recently realized the importance of modeling electric loads in steady-state and dynamic state in the time domain. Predictive analysis of loads, the basis of electrical system planning, is still one of the most difficult aspects to evaluate due to the complexity of developing a suitable dynamic model. The most challenging aspect is to find a suitable way to represent the aggregate behavior of a large number of devices with vastly different sizes and characteristics that, in their totality, constitute a housing unit [1]. In addition, there is variability and uncertainty related to environmental, geographic, economic, political and social conditions that make it extremely challenging to construct a universally valid algorithm. The purpose of this paper is to present a model called Enanched Bottom-Up Model (EBM) that can produce load profiles that are extremely consistent with real ones, minimizing errors and thus allowing for a valuable tool for planning residential electrical systems. This model, as will be shown, is highly dynamic, a feature that allows it to be constantly updated to provide actual results and consider the technological evolution of residential units. To evaluate the effectiveness of the EBM, a case study will then be presented in which a real load profile, realized by direct measurement, is compared with an indirect load profile generated by the algorithm that holds the same conditions, so as to verify how different the realized curves are from each other.
2023
2023 International Conference on Clean Electrical Power, ICCEP 2023
energy communities; smart microgrids; load profiles; bottom-up; enhanced bottom-up; load demand management.
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Electrical Load Profiles for Residential Buildings: Enhanced Bottom-Up Model (EBM) / Loggia, Riccardo; Flamini, Alessandro; Massaccesi, Andrea; Moscatiello, Cristina; Galasso, Alessandro; Martirano, Luigi. - (2023), pp. 635-640. (Intervento presentato al convegno 2023 International Conference on Clean Electrical Power, ICCEP 2023 tenutosi a Terrasini, Italy) [10.1109/ICCEP57914.2023.10247473].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1690502
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