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- Nammouchi, Amal, et al.
(författare)
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Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management
- 2021
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Ingår i: 2021 21St Ieee International Conference On Environment And Electrical Engineering And 2021 5Th Ieee Industrial And Commercial Power Systems Europe (Eeeic/I&Cps Europe). - : IEEE. - 9781665436137
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Konferensbidrag (refereegranskat)abstract
- Towards zero CO2 emissions society, large shares of renewable energy sources and storage systems are integrated into microgrids as part of the electrical grids for energy exchange aiming to effectively reduce the stress from the transmission grid. However, energy management within and across microgrids is complicated due to many uncertainties such as imprecise knowledge on energy production and demand, which makes energy optimization challenging. In this paper, we present an open architecture that uses machine learning algorithms at the edge to predict energy consumption and production for energy management in smart microgrids. Such predictions are aggregated across different prosumers at a centralized marketplace in the Cloud using Kafka Streams and OpenSource IoT platforms. Using pluggable optimization algorithms, different microgrids can implement different strategies for real-time optimal energy schedules. The proposed architecture is evaluated in terms of scalability and accuracy of predictions. Our heuristics can effectively optimize medium-sized microgrids.
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| 2. |
- Nammouchi, Amal, et al.
(författare)
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Multi-Objective Microgrid Control Using Deep Reinforcement Learning
- 2024
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Optimizing renewable energy usage in smart microgrids that contain photovoltaic production and battery storage is important due to the potential to reduce overall CO2 emissions and thus lead to more environmental friendly energy systems. However, due to the complex nature of energy management in smart grids and the volatile nature of energy production from PV systems, the problem is complex to solve. In this work we aim to optimize the energy in a microgrid comprising six houses using a digital twin based approach based on Deep Reinforcement Learning. We develop a Soft Actor-Critic (SAC) agent to address this intricate challenge, with the aim to simultaneously reduce emissions, maintain user comfort, while maximizing grid efficiency and resiliency to cope with spurious grid outages. We propose and evaluate different reward functions that guide the agent in finding its optimal policy.. Furthermore, we discuss the implications of our results and outline potential future steps, envisioning ongoing refinement and advancements in our pursuit of optimal solutions for the complex interplay of severaal objectives in microgrid management.
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| 3. |
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| 4. |
- Nammouchi, Amal, et al.
(författare)
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Robust Operation of Energy Communities in the Italian Incentive System
- 2023
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Ingår i: 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350396782 - 9798350396799
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Konferensbidrag (refereegranskat)abstract
- In this paper, we address the optimal operation of energy communities, under energy production and consumption uncertainties. In the nominal case, the operational problem is formulated as the maximization of the profit of the community over a given time horizon. Inspired by the regulation adopted in Italy since 2020, the profit includes an incentive for the self-consumption realized at the community level in each time period. In the presence of uncertainties, we use a robust formulation aiming at maximizing the worst profit achievable when energy production and consumption deviate from their nominal values. The model results in a robust scheduling policy of battery charging/discharging, guaranteeing feasible operation of the community in all scenarios of the uncertainty set. On the other hand, numerical results show that the nominal scheduling policy may suffer from a high constraint violation probability.
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| 5. |
- Nammouchi, Amal, et al.
(författare)
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Robust opportunistic optimal energy management of a mixed microgrid under asymmetrical uncertainties
- 2023
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Ingår i: Sustainable Energy, Grids and Networks. - : Elsevier. - 2352-4677. ; 36
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Tidskriftsartikel (refereegranskat)abstract
- Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.
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