A Federated DRL Approach for Smart Micro-Grid Energy Control with Distributed Energy Resources
| Autor: | Farhad Rezazadeh, Nikolaos bartzoudis |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Networking and Internet Architecture (cs.NI)
FOS: Computer and information sciences Deep reinforcement learning Internet of things Distributed Energy Resources Energy Learning systems Commerce Dynamic loads Energy management Federated learning Deep learning Smart grid Renewable energy resources Carbon footprint Reinforcement learning approach Actor critic Reinforcement learnings Computer Science - Networking and Internet Architecture Soft actor-critic Energy management systems Smart power grids Reinforcement learning Smart Micro Grids |
| Zdroj: | 2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) |
| Popis: | The prevalence of the Internet of things (IoT) and smart meters devices in smart grids is providing key support for measuring and analyzing the power consumption patterns. This approach enables end-user to play the role of prosumers in the market and subsequently contributes to diminish the carbon footprint and the burden on utility grids. The coordination of trading surpluses of energy that is generated by house renewable energy resources (RERs) and the supply of shortages by external networks (main grid) is a necessity. This paper proposes a hierarchical architecture to manage energy in multiple smart buildings leveraging federated deep reinforcement learning (FDRL) with dynamic load in a distributed manner. Within the context of the developed FDRL-based framework, each agent that is hosted in local building energy management systems (BEMS) trains a local deep reinforcement learning (DRL) model and shares its experience in the form of model hyperparameters to the federation layer in the energy management system (EMS). Simulation studies are conducted using one EMS and up to twenty smart houses that are equipped with photovoltaic (PV) systems and batteries. This iterative training approach enables the proposed discretized soft actor-critic (SAC) agents to aggregate the collected knowledge to expedite the overall learning procedure and reduce costs and CO2 emissions, while the federation approach can mitigate privacy breaches. The numerical results confirm the performance of the proposed framework under different daytime periods, loads, and temperatures. 7 pages, 6 figures, accepted for publication at IEEE CAMAD 2022 |
| Databáze: | OpenAIRE |
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