Efficient Allocation of Harvested Energy at the Edge by Building a Tangible Micro-Grid—The Texas Case
| Autor: | R. Venkatesha Prasad, Nikolaos Kouvelas |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
game theory
Computer Networks and Communications Renewable Energy Sustainability and the Environment Computer science 020209 energy 020208 electrical & electronic engineering 02 engineering and technology Energy consumption Environmental economics 7. Clean energy energy allocation strategies water filling Smart grid Information and Communications Technology Energy flow 0202 electrical engineering electronic engineering information engineering Resource management Enhanced Data Rates for GSM Evolution Information flow (information theory) Micro-girds harvested energy Energy (signal processing) social welfare clustering |
| Zdroj: | IEEE Transactions on Green Communications and Networking IEEE Transactions on Green Communications and Networking, 5(1) |
| ISSN: | 2473-2400 |
| DOI: | 10.1109/tgcn.2020.3047432 |
| Popis: | The electricity grid, using Information and Communication Technology, is transformed into Smart Grid (SG), which is highly efficient and responsive, promoting two-way energy and information flow between energy-distributors and consumers. Many consumers are becoming prosumers by also harvesting energy. The trend is to form small communities of consumers/prosumers, leading to Micro-grids (MG) to manage energy locally. MGs are parts of SG that decentralize the energy flow, allocating the excess of harvested energy within the community. Energy allocation amongst them must solve certain issues viz., 1) balancing supply/demand within MGs; 2) how allocating energy to a user affects his/her community; and 3) what are the economic benefits for users. To address these issues, we propose six Energy Allocation Strategies (EASs) for MGs - ranging from simple to optimal and their combinations. We maximize the usage of harvested energy within the MG. We form household-groups sharing similar characteristics to apply EASs by analyzing energy and socioeconomic data thoroughly. We propose four evaluation metrics and evaluate our EASs on data acquired from 443 households over a year. By prioritizing specific households, we increase the number of fully served households to 81% compared to random sharing. By combining EASs, we boost the social welfare parameter by 49%. |
| Databáze: | OpenAIRE |
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