Skip to main content

2022 | OriginalPaper | Buchkapitel

An Incentive Dispatch Algorithm for Utilization-Perfect EV Charging Management

verfasst von : Lo Pang-Yun Ting, Po-Hui Wu, Hsiu-Ying Chung, Kun-Ta Chuang

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Due to the rapid growth of electric vehicles (EVs), the charging scheduling of EVs has become highly important. In order to reduce the total operating cost, how to arrange the charging of each EV becomes the main issue. However, existing scheduling methods usually obtain schedules without considering EVs users’ charging willingness, which will let EVs users be reluctant to follow the arranged charging schedule, thereby incurring low charging utilization and high operational overhead. To solve this problem, we devise an online charging registration mechanism, an incentive-based framework called POSIT, to provide a feasible schedule for different EVs to enhance the quality of user experience. In the proposed mechanism, the charging scheduler will provide a relevant reward (as an incentive) for users to properly enhance users’ willingness to accept the arranged schedule. In addition, the interactive learning is adopted to improve the next recommendation based on the user’s feedback. The POSIT framework is able to satisfy the energy demand of EVs charging and the commercial building. The implemented experiments indicate that the proposed framework can not only increase the charging utilization, but also can significantly reduce the electrical operating costs and increase the revenue at the cost of small incentives.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Fußnoten
1
For ease of explanation, we assume the equal charging speed in our discussion. It is believed that the implementation issue of variant charging speeds can be seamlessly extended in POSIT.
 
Literatur
1.
Zurück zum Zitat Ausgrid. Solar home electricity data (2012) Ausgrid. Solar home electricity data (2012)
2.
Zurück zum Zitat Gossen, H.H.: Gossen, hermann heinrich: Entwickelung der gesetze des menschlichen verkehrs, und der daraus fließenden regeln für menschliches handeln. Die 100 wichtigsten Werke der Ökonomie (2019) Gossen, H.H.: Gossen, hermann heinrich: Entwickelung der gesetze des menschlichen verkehrs, und der daraus fließenden regeln für menschliches handeln. Die 100 wichtigsten Werke der Ökonomie (2019)
3.
Zurück zum Zitat International Energy Agency (IEA). IEA global annual hybrid, plug-in hybrid and battery electric vehicle sales 2021 (2020) International Energy Agency (IEA). IEA global annual hybrid, plug-in hybrid and battery electric vehicle sales 2021 (2020)
4.
Zurück zum Zitat Jin, C., Tang, J., Ghosh, P.K.: Optimizing electric vehicle charging with energy storage in the electricity market. IEEE Trans. Smart Grid 4, 311–320 (2013)CrossRef Jin, C., Tang, J., Ghosh, P.K.: Optimizing electric vehicle charging with energy storage in the electricity market. IEEE Trans. Smart Grid 4, 311–320 (2013)CrossRef
5.
Zurück zum Zitat Kabir, M.E., Assi, C.M., Tushar, M.H.K., Yan, J.: Optimal scheduling of EV charging at a solar power-based charging station. IEEE Syst. J. 14, 4221–4231 (2020)CrossRef Kabir, M.E., Assi, C.M., Tushar, M.H.K., Yan, J.: Optimal scheduling of EV charging at a solar power-based charging station. IEEE Syst. J. 14, 4221–4231 (2020)CrossRef
6.
Zurück zum Zitat Kuan, C.-P., Young, K.Y.: Reinforcement learning and robust control for robot compliance tasks. J. Intell. Rob. Syst. 23, 165–182 (1998)CrossRef Kuan, C.-P., Young, K.Y.: Reinforcement learning and robust control for robot compliance tasks. J. Intell. Rob. Syst. 23, 165–182 (1998)CrossRef
7.
Zurück zum Zitat Lee, Z.J., Li, T., Low, S.H.: ACN-data: analysis and applications of an open EV charging dataset. In: Proceedings of the Tenth ACM International Conference on Future Energy Systems (2019) Lee, Z.J., Li, T., Low, S.H.: ACN-data: analysis and applications of an open EV charging dataset. In: Proceedings of the Tenth ACM International Conference on Future Energy Systems (2019)
8.
Zurück zum Zitat Li, H., Wan, Z., He, H.: Constrained EV charging scheduling based on safe deep reinforcement learning. IEEE Trans. Smart Grid 11, 2427–2439 (2020)CrossRef Li, H., Wan, Z., He, H.: Constrained EV charging scheduling based on safe deep reinforcement learning. IEEE Trans. Smart Grid 11, 2427–2439 (2020)CrossRef
9.
Zurück zum Zitat Liu, Y., Hou, D., Bao, J., Qi, Y.: Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network. In: WISA (2017) Liu, Y., Hou, D., Bao, J., Qi, Y.: Multi-step ahead time series forecasting for different data patterns based on LSTM recurrent neural network. In: WISA (2017)
10.
Zurück zum Zitat Kuo, C.-C., Piedad, E.J.: A 12-month data of hourly energy consumption levels from a commercial-type consumer. Mendeley Data (2018) Kuo, C.-C., Piedad, E.J.: A 12-month data of hourly energy consumption levels from a commercial-type consumer. Mendeley Data (2018)
11.
Zurück zum Zitat Satterfiled, C., Nigro, N.: Public EV charging business models for retail site hosts (2020) Satterfiled, C., Nigro, N.: Public EV charging business models for retail site hosts (2020)
12.
Zurück zum Zitat Shah, D., Xie, Q.: Q-learning with nearest neighbors. In: NeurIPS (2018) Shah, D., Xie, Q.: Q-learning with nearest neighbors. In: NeurIPS (2018)
13.
Zurück zum Zitat Sun, X., Qiu, J.: A customized voltage control strategy for electric vehicles in distribution networks with reinforcement learning method. IEEE Trans. Industr. Inf. 17, 6852–6863 (2021)CrossRef Sun, X., Qiu, J.: A customized voltage control strategy for electric vehicles in distribution networks with reinforcement learning method. IEEE Trans. Industr. Inf. 17, 6852–6863 (2021)CrossRef
14.
Zurück zum Zitat Wang, R., Wang, P., Xiao, G.: Two-stage mechanism for massive electric vehicle charging involving renewable energy. IEEE Trans. Veh. Technol. 65, 4159–4171 (2016)CrossRef Wang, R., Wang, P., Xiao, G.: Two-stage mechanism for massive electric vehicle charging involving renewable energy. IEEE Trans. Veh. Technol. 65, 4159–4171 (2016)CrossRef
15.
Zurück zum Zitat Yan, Q., Zhang, B., Kezunovic, M.: Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation. IEEE Trans. Smart Grid 10, 2096–2106 (2019)CrossRef Yan, Q., Zhang, B., Kezunovic, M.: Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation. IEEE Trans. Smart Grid 10, 2096–2106 (2019)CrossRef
16.
Zurück zum Zitat Yang, Yu., Jia, Q.-S., Guan, X., Zhang, X., Qiu, Z., Deconinck, G.: Decentralized EV-based charging optimization with building integrated wind energy. IEEE Trans. Autom. Sci. Eng. 16, 1002–1017 (2019)CrossRef Yang, Yu., Jia, Q.-S., Guan, X., Zhang, X., Qiu, Z., Deconinck, G.: Decentralized EV-based charging optimization with building integrated wind energy. IEEE Trans. Autom. Sci. Eng. 16, 1002–1017 (2019)CrossRef
17.
Zurück zum Zitat Zhou, Y., Kumar, R.E., Tang, S.: Incentive-based distributed scheduling of electric vehicle charging under uncertainty. IEEE Trans. Power Syst. 34, 3–11 (2019)CrossRef Zhou, Y., Kumar, R.E., Tang, S.: Incentive-based distributed scheduling of electric vehicle charging under uncertainty. IEEE Trans. Power Syst. 34, 3–11 (2019)CrossRef
Metadaten
Titel
An Incentive Dispatch Algorithm for Utilization-Perfect EV Charging Management
verfasst von
Lo Pang-Yun Ting
Po-Hui Wu
Hsiu-Ying Chung
Kun-Ta Chuang
Copyright-Jahr
2022
DOI
https://doi.org/10.1007/978-3-031-05981-0_11

Premium Partner