Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage
Microgrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10184412/ |
| _version_ | 1797773367638491136 |
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| author | Kiraseya Preusser Anke Schmeink |
| author_facet | Kiraseya Preusser Anke Schmeink |
| author_sort | Kiraseya Preusser |
| collection | DOAJ |
| description | Microgrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs within the microgrid are met without wasting electricity. With higher requirements for electric vehicle charging stations (EVCSs), schedulers are essential to ensure EV charging demands are met while being profitable and flattening peak load on the main power grid (MPG). This paper introduces two novel microgrid models, combining energy generated by a DER, the possibility of storage with an energy storage system (ESS), a load entity in the form of an EVCS and electricity trading with the MPG. The model incorporates all important environment parameters created by these players in an intelligent way that keeps the action space relatively small and thus avoiding the problems associated with a high computational complexity. These models are proven to successfully shift the load from the MPG, while still providing high customer satisfaction and throughput, in a profitable way, despite costs incurred by the DER. Instead of relying on models, real data is used, ensuring that the model is robust. Additional real world stress tests are carried out with respect to electricity costs, wind energy generation, and charging rates. Reinforcement learning is implemented to find the optimal scheduler by maximizing overall profits. In all cases considered a self-sustaining system is established, that is a more profitable and reliable EVCS. |
| first_indexed | 2024-03-12T22:04:31Z |
| format | Article |
| id | doaj.art-cb1f5e82b256487f9cb5b9bc13372e23 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:04:31Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cb1f5e82b256487f9cb5b9bc13372e232023-07-24T23:00:28ZengIEEEIEEE Access2169-35362023-01-0111734357344710.1109/ACCESS.2023.329599710184412Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy StorageKiraseya Preusser0https://orcid.org/0000-0002-1820-8841Anke Schmeink1Chair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, GermanyChair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, GermanyMicrogrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs within the microgrid are met without wasting electricity. With higher requirements for electric vehicle charging stations (EVCSs), schedulers are essential to ensure EV charging demands are met while being profitable and flattening peak load on the main power grid (MPG). This paper introduces two novel microgrid models, combining energy generated by a DER, the possibility of storage with an energy storage system (ESS), a load entity in the form of an EVCS and electricity trading with the MPG. The model incorporates all important environment parameters created by these players in an intelligent way that keeps the action space relatively small and thus avoiding the problems associated with a high computational complexity. These models are proven to successfully shift the load from the MPG, while still providing high customer satisfaction and throughput, in a profitable way, despite costs incurred by the DER. Instead of relying on models, real data is used, ensuring that the model is robust. Additional real world stress tests are carried out with respect to electricity costs, wind energy generation, and charging rates. Reinforcement learning is implemented to find the optimal scheduler by maximizing overall profits. In all cases considered a self-sustaining system is established, that is a more profitable and reliable EVCS.https://ieeexplore.ieee.org/document/10184412/Distributed energy resources (DER)electric vehicles (EV)energy storage system (ESS)microgridreinforcement learning (RL)scheduling |
| spellingShingle | Kiraseya Preusser Anke Schmeink Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage IEEE Access Distributed energy resources (DER) electric vehicles (EV) energy storage system (ESS) microgrid reinforcement learning (RL) scheduling |
| title | Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage |
| title_full | Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage |
| title_fullStr | Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage |
| title_full_unstemmed | Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage |
| title_short | Energy Scheduling for a DER and EV Charging Station Connected Microgrid With Energy Storage |
| title_sort | energy scheduling for a der and ev charging station connected microgrid with energy storage |
| topic | Distributed energy resources (DER) electric vehicles (EV) energy storage system (ESS) microgrid reinforcement learning (RL) scheduling |
| url | https://ieeexplore.ieee.org/document/10184412/ |
| work_keys_str_mv | AT kiraseyapreusser energyschedulingforaderandevchargingstationconnectedmicrogridwithenergystorage AT ankeschmeink energyschedulingforaderandevchargingstationconnectedmicrogridwithenergystorage |