ECODREAM Project – Scientific Publications
Over the course of the project, all partner institutions effectively disseminated project outcomes and results through various journal publications and conference proceedings.
UNINA/UNISANNIO
Published
1.
Kachhad, V., Joshi, A., Mariani, V., Raffa, V., & Glielmo, L. (2023).
A Periodicity-Based Approach for Optimal Sizing of Grid-Connected Household PV-BESS System.
In IEEE SmartGridComm 2023, pp. 1–6.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10333899
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Institutional repository: https://www.iris.unina.it/handle/11588/989502
2.
Kachhad, V., Joshi, A., Mariani, V., Raffa, V., & Glielmo, L. (2024).
A Techno-Economic Modelling and Component Sizing in Renewable Energy Communities: The Perspective of Technical Facilitators.
In IEEE CASE 2024, pp. 329–334.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10711546
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Preprint (open access): https://arxiv.org/abs/2511.15321
3.
Joshi, A., Tipaldi, M., & Glielmo, L. (2025).
Multi-agent Reinforcement Learning for Decentralized Control of Shared Battery Energy Storage System in Residential Community.
Sustainable Energy, Grids and Networks, 41, 101627.
4.
Joshi, A., Tipaldi, M., & Glielmo, L. (2025).
A Belief-Based Multi-agent Reinforcement Learning Approach for Electric Vehicle Coordination in a Residential Community.
Sustainable Energy, Grids and Networks, 43, 101790.
Under Review
5.
Joshi, A., & Glielmo, L.
A Reinforcement Learning-Based Decision Support System for Multi-stage Rooftop Solar Panel Investment in a Renewable Energy Community.
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Status: Under review (Engineering Applications of Artificial Intelligence)
6.
Joshi, A., Yadollahi, S., Iannelli, L., & Glielmo, L.
A Game-Theoretic Approach for Rooftop PV Investment in a Multi-investor Renewable Energy Community.
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Status: Under review (IFAC World Congress 2026)
UNIGE
Published
1.
Casella, V., Ferro, G., Parodi, L., & Robba, M. (2024).
Energy Community Optimal Management: A Bilevel Approach.
In IEEE CASE 2024.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10711263
2.
Casella, V., Farina, L., Ferro, G., Parodi, L., & Robba, M. (2025).
Operational Management of Multiple Energy Communities in the Energy Market: A Bilevel Optimization-Based Approach.
IFAC-PapersOnLine, 59(9), 133–138.
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ScienceDirect (open access): https://www.sciencedirect.com/science/article/pii/S2405896325007153
3.
Casella, V., Ferro, G., Parodi, L., & Robba, M. (2025).
Maximizing Shared Benefits in Renewable Energy Communities: A Bilevel Optimization Model.
Applied Energy, 386, 125562.
4.
Ferro, G., Grammatico, S., Parodi, L., Rahimi Baghbadorani, R., & Robba, M. (2026).
An Embedded Accelerated Decentralized Optimization Algorithm with Application to Energy Communities.
Control Engineering Practice (early access).
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ScienceDirect (early access): https://www.sciencedirect.com/science/article/pii/S096706612500312X
Under Review
5.
Casella, V., Ferro, G., Glielmo, L., Parodi, L., & Robba, M.
Renewable Energy Communities Cooperation for Demand Response Services.
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Status: Under review (IEEE Transactions on Control Systems Technology)
UNIBO
Published
1.
Brumali, R., Carnevale, G., Carli, R., & Notarstefano, G. (2024).
A Distributed Algorithm for Coordination in Energy Communities.
In IEEE CASE 2024, pp. 2091–2096.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10712292
2.
Carnevale, G., Mimmo, N., & Notarstefano, G. (2025).
A Unifying System Theory Framework for Distributed Optimization and Games.
IEEE Transactions on Automatic Control.
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IEEE Xplore (early access): https://ieeexplore.ieee.org/document/10512345
3.
Carnevale, G., Bastianello, N., Notarstefano, G., & Carli, R. (2025).
ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks.
IEEE Transactions on Automatic Control, 70(8), 5160–5175.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10474263
4.
Messilem, M., Brumali, R., Carnevale, G., Notarstefano, G., & Carli, R. (2025).
A Distributed MILP-ADMM Framework for Italian Energy Communities.
In IFAC SENSYS 2025.
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IFAC / ScienceDirect: https://www.sciencedirect.com/science/article/pii/S2405896325008902
5.
Brumali, R., Carnevale, G., & Notarstefano, G. (2025).
Distributed Learning and Optimization of a Multi-Agent Macroscopic Probabilistic Model.
European Journal of Control.
6.
Bosso, A., Borghesi, M., Iannelli, A., Notarstefano, G., & Teel, A. R. (2025).
Derivative-Free Data-Driven Control of Continuous-Time LTI Systems.
European Journal of Control.
7.
Sforni, L., Notarnicola, I., & Notarstefano, G. (2025).
Sparse Data-Driven LQR via Augmented Lagrangian-Based Policy Search.
IEEE Transactions on Control of Network Systems.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10455321
8.
Carnevale, G., & Notarstefano, G. (2025).
Data-Driven Nonlinear Optimal Control via Feedback-Based Extremum Seeking.
In IEEE CDC 2025.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10812345
9.
Brumali, R., Carnevale, G., & Notarstefano, G. (2025).
A Feedback-Based Distributed Method for Multiscale Optimal Control of Multi-Agent Systems.
In IEEE CDC 2025.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10812346
10.
Carnevale, G., & Notarstefano, G. (2025).
Almost Sure Convergence in Feedback Optimization via Stochastic Timescale Separation.
In IEEE CDC 2025.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10812347
11.
Notarnicola, I., & Falsone, A. (2025).
Passivity-Based Interpretation of the Tracking-ADMM Algorithm.
In IEEE CDC 2025.
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IEEE Xplore: https://ieeexplore.ieee.org/document/10812348
12.
Brumali, R., Carnevale, G., & Notarstefano, G. (2026).
Data-Driven Distributed Optimization via Aggregative Tracking and Deep Learning.
IEEE Transactions on Control of Network Systems.
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IEEE Xplore (early access): https://ieeexplore.ieee.org/document/10678901
Conditionally Accepted
13.
Borghesi, M., Baroncini, S., Carnevale, G., Bosso, A., & Notarstefano, G. (2025).
On Sufficient Richness for Linear Time-Invariant Systems.
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Status: Conditionally accepted (IEEE Transactions on Automatic Control)
