Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/94A88EB4-A0FC-4217-8E0B-FC6D8E8F4ED9 | ||
| Year | 2022 | ||
| Type of Item | Master Thesis | ||
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| Bibliographic Citation | Stavros Orfanoudakis, "A novel smart grid flexibility aggregation framework", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022 https://doi.org/10.26233/heallink.tuc.94107 | ||
| Appears in Collections |
The increasing number of Distributed Energy Resources (DERs) in the emerging Smart Grid, has created an imminent need for intelligent multiagent frameworks able to utilize these assets efficiently. Additionally, the constant depletion of fossil fuels and carbon dioxide emissions have rendered the transition to Smart Grid a definite necessity. Electric vehicles, Battery Energy Storage Systems (BESS), interruptible load users, and renewable energy generators, such as solar panels and wind turbines, are only a few of the most common examples of DERs that will help in the transition to Smart Grid. In particular, DERs are essential for the smooth operation of the future Smart Grid since DERs enable flexible loads to be utilized, hence improving the stability of the Grid and enabling consumer-side demand management. To address the significant aforementioned problems, in this MSc thesis we propose a novel DER aggregation framework, encompassing a multiagent architecture and various types of mechanisms for the effective management and efficient integration of DERs in the Grid. One critical component of our architecture is the Local Flexibility Estimators (LFEs) agents, which are key for offloading the Aggregator from serious or resource-intensive responsibilities---such as addressing privacy concerns and predicting the accuracy of DER statements regarding their offered demand response services. The proposed aggregation framework allows the formation of efficient and effective LFE cooperatives. To this end, we developed and deployed a variety of cooperative member selection mechanisms, including {\em (a)} scoring rules, and {\em (b)} (deep) reinforcement learning. We use data from the well-known PowerTAC simulator to systematically evaluate our framework in various scenarios based on Smart Grid settings, so the efficiency of the framework can be properly assessed. Our experiments verify its effectiveness for incorporating heterogeneous DERs into the Grid in an efficient manner---showing that the use of appropriate mechanisms results in higher payments for competent LFEs managed by the Aggregator.
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