Short-Term Predictive Power Management of PV-Powered Nanogrids
Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9163118/ |
| _version_ | 1818616932140580864 |
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| author | Sangkeum Lee Hojun Jin Luiz Felipe Vecchietti Junhee Hong Dongsoo Har |
| author_facet | Sangkeum Lee Hojun Jin Luiz Felipe Vecchietti Junhee Hong Dongsoo Har |
| author_sort | Sangkeum Lee |
| collection | DOAJ |
| description | Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level. |
| first_indexed | 2024-12-16T16:57:39Z |
| format | Article |
| id | doaj.art-220a25c576704ee38ee0940aa0a7930b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:57:39Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-220a25c576704ee38ee0940aa0a7930b2022-12-21T22:23:50ZengIEEEIEEE Access2169-35362020-01-01814783914785710.1109/ACCESS.2020.30152439163118Short-Term Predictive Power Management of PV-Powered NanogridsSangkeum Lee0https://orcid.org/0000-0001-6918-124XHojun Jin1https://orcid.org/0000-0002-3751-4983Luiz Felipe Vecchietti2https://orcid.org/0000-0003-2862-6200Junhee Hong3https://orcid.org/0000-0003-1285-1454Dongsoo Har4https://orcid.org/0000-0002-6949-1739The Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, Daejeon, South KoreaThe Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, Daejeon, South KoreaThe Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, Daejeon, South KoreaDepartment of Energy IT, Gachon University, Seongnam, South KoreaThe Cho Chun Shik Graduate School of Green Transportation, Korea Advanced Institute of Science and Technology, Daejeon, South KoreaOptimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level.https://ieeexplore.ieee.org/document/9163118/Power managementnanogridpeak load shiftingPV powerLSTM network |
| spellingShingle | Sangkeum Lee Hojun Jin Luiz Felipe Vecchietti Junhee Hong Dongsoo Har Short-Term Predictive Power Management of PV-Powered Nanogrids IEEE Access Power management nanogrid peak load shifting PV power LSTM network |
| title | Short-Term Predictive Power Management of PV-Powered Nanogrids |
| title_full | Short-Term Predictive Power Management of PV-Powered Nanogrids |
| title_fullStr | Short-Term Predictive Power Management of PV-Powered Nanogrids |
| title_full_unstemmed | Short-Term Predictive Power Management of PV-Powered Nanogrids |
| title_short | Short-Term Predictive Power Management of PV-Powered Nanogrids |
| title_sort | short term predictive power management of pv powered nanogrids |
| topic | Power management nanogrid peak load shifting PV power LSTM network |
| url | https://ieeexplore.ieee.org/document/9163118/ |
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