Chao Luo, Yih-Fang Huang, Vijay Gupta
arXiv 9 Jan 2018 · eess.SP · 16 citations (OpenAlex)
arXiv:1801.02783 · PDF · DOI · OpenAlex · Extracted main text
This paper presents a dynamic pricing and energy management framework for electric vehicle (EV) charging service providers. To set the charging prices, the service providers faces three uncertainties: the volatility of wholesale electricity price, intermittent renewable energy generation, and spatial-temporal EV charging demand. The main objective of our work here is to help charging service providers to improve their total profits while enhancing customer satisfaction and maintaining power grid stability, taking into account those uncertainties. We employ a linear regression model to estimate the EV charging demand at each charging station, and introduce a quantitative measure for customer satisfaction. Both the greedy algorithm and the dynamic programming (DP) algorithm are employed to derive the optimal charging prices and determine how much electricity to be purchased from the wholesale market in each planning horizon. Simulation results show that DP algorithm achieves an increased profit (up to 9%) compared to the greedy algorithm (the benchmark algorithm) under certain scenarios. Additionally, we observe that the integration of a low-cost energy storage into the system can not only improve the profit, but also smooth out the charging price fluctuation, protecting the end customers from the volatile wholesale market.
appendix boundary found by appendix_titled_section at “\uppercase{Appendix}” · 96% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bertsekas, D (2000) Dynamic Programming and Optimal Control (2nd ed.) | 0.511 | 2 | 1 | 100% |
| 2 | Rahbari-Asr, N., Chow, M.-Y., Yang, Z., and Chen, J (2013) Network cooperative distributed pricing control system for large-scale optimal charging of phevs/pevs | 0.405 | 1 | 1 | 100% |
| 3 | Ban, D., Michailidis, G., and Devetsikiotis, M (2012) Demand response control for phev charging stations by dynamic price adjustments | 0.405 | 1 | 1 | 100% |
| 4 | Cormen, T., Leiserson, C., Rivest, R., and Stein, C (2001) Introduction to Algorithm (2nd ed.) | 0.405 | 1 | 1 | 100% |
| 5 | IEC (2007) Efficient electrical energy transmission and distribution | 0.405 | 1 | 1 | 100% |
| 6 | Fahrioglu, M., Fern, M., and Alvarado, F (1999) Designing cost effective demand management contracts using game theory | 0.405 | 1 | 1 | 100% |
| 7 | Faranda, R., Pievatolo, A., and Tironi, E (2007) Load shedding: A new proposal | 0.405 | 1 | 1 | 100% |
| 8 | Han, Y., Chen, Y., Han, F., and Liu, K. J. R (2012) An optimal dynamic pricing and schedule approach in v2g | 0.405 | 1 | 1 | 100% |
| 9 | Huisman, R., Huurman, C., and Mahieu, R (2007) Hourly electricity prices in day-ahead markets | 0.405 | 1 | 1 | 100% |
| 10 | Kinter-Meyer, M., Schneider, K., and Pratt, R (2007) Impacts assessment of plug-in hybrid electric vehicles on electric utilities and regional u.s. power grids: Part i:technical ana… | 0.405 | 1 | 1 | 100% |
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