Marialaura Di Somma

Prof. Dr. Marialaura Di Somma

Department of Industrial Engineering, University of Naples Federico II, Naples, Campania, Italy
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2018 in Renewable Energy
Verified
Journal Article
Closed access
Marialaura Di SommaMarialaura Di Somma
Marialaura Di Somma
GGG. Graditi
G. Graditi
EHE. Heydarian-Forushani
E. Heydarian-Forushani
MSM. Shafie-Khah
M. Shafie-Khah
PSPierluigi Siano
Pierluigi Siano

24 April 2018 in IEEE Transactions on Industrial Electronics
Verified
Journal Article
Closed access
Marialaura Di SommaMarialaura Di Somma
Marialaura Di Somma
Giorgio GraditiGiorgio Graditi
Giorgio Graditi
PSPierluigi Siano
Pierluigi Siano
The penetration of distributed energy resources (DER), including distributed generators, storage devices, and demand response (DR) is growing worldwide, encouraged by environmental policies and decreasing costs. To enable DER local integration, new energy players as aggregators appeared in the electricity markets. This player, acting toward the grid as one entity, can offer new services to the electricity market and the system operator by aggregating flexible DER involving both DR and generation resources. In this paper, an optimization model is provided for participation of a DER aggregator in the day-ahead market in the presence of demand flexibility. This player behaves as an energy aggregator, which manages energy and financial interactions between the market and DER organized in local energy systems (LES), which are in charge to satisfy the multienergy demand of a set of building clusters with flexible demand. A stochastic mixed-integer linear programming problem is formulated by considering uncertainties of intermittent DER facilities and day-ahead market price, to find the optimal bidding strategies while maximizing the expected aggregator's profit. Numerical results show that the method is efficient in finding the bidding curves in the day-ahead market through the optimal management of flexibility requests sent to clusters, as well as of DER in LES and interactions among LES.

1 October 2015 in Energy Conversion and Management
Verified
Journal Article
Closed access
Marialaura Di SommaMarialaura Di Somma
Marialaura Di Somma
BYB. Yan
B. Yan
Nicola BiancoNicola Bianco
Nicola Bianco
GGG. Graditi
G. Graditi
PLP.B. Luh
P.B. Luh
VNV. Naso
V. Naso

1 October 2017 in Applied Energy
Verified
Journal Article
Closed access
Marialaura Di SommaMarialaura Di Somma
Marialaura Di Somma
BYB. Yan
B. Yan
NBN. Bianco
N. Bianco
GGG. Graditi
G. Graditi
PLP.B. Luh
P.B. Luh
VNV. Naso
V. Naso

1 May 2016 in Applied Thermal Engineering
Verified
Journal Article
Closed access
Marialaura Di SommaMarialaura Di Somma
Marialaura Di Somma
BYBing Yan
Bing Yan
NBNicola Bianco
Nicola Bianco
PLPeter B. Luh
Peter B. Luh
GGGiorgio Graditi
Giorgio Graditi
VNVincenzo Naso
Vincenzo Naso

1 September 2020 in Electric Power Systems Research
Verified
Journal Article
Closed access
BYBing Yan
Bing Yan
Marialaura Di SommaMarialaura Di Somma
Marialaura Di Somma
Giorgio GraditiGiorgio Graditi
Giorgio Graditi
PLPeter B. Luh
Peter B. Luh