Chao Luo, Yih-Fang Huang, Vijay Gupta
arXiv 7 Jan 2018 · eess.SP · 2 citations (OpenAlex)
arXiv:1801.02128 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties --- charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. We propose a new metric to assess the impact on power grid without solving complete power flow equations. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms --- stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm) --- are applied to derive the pricing and electricity procurement policy. A Pareto front of the multiobjective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.
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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 | C.-L. Hwang, and A. Masud, Multiple Objective Decision Making � Meth… (1979) | 0.644 | 2 | 2 | 100% |
| 2 | I. Kim, and O. Weck, “Adaptive weighted sum method for multiobjectiv… (2006) | 0.511 | 2 | 1 | 100% |
| 3 | A. Dobson, and A. Barnett, An Introduction to Generalized Linear Mod… (2008) | 0.405 | 1 | 1 | 100% |
| 4 | A. Kerr, “Stochastic Utility Maximising Dynamic Programming Applied… (2003) | 0.405 | 1 | 1 | 100% |
| 5 | A. Simpson, “Cost-benefit Analysis of Plugin Hybrid Electric Vehicle… (2006) | 0.405 | 1 | 1 | 100% |
| 6 | C. Luo, Y.-F. Huang, and V. Gupta, “A Consumer Behavior Based Approa… (2015) self | 0.405 | 1 | 1 | 100% |
| 7 | C. Ozelkan, A. Galambosi, E. Fernandez-Gaucherand, and L. Duckstein,… (1997) | 0.405 | 1 | 1 | 100% |
| 8 | C. Zhao, U. Topcu, and S. Low, “Optimal Load Control via Frequency M… (2013) | 0.405 | 1 | 1 | 100% |
| 9 | D. Ban, G. Michailidis, and M. Devetsikiotis, “Demand Response Contr… (2012) | 0.405 | 1 | 1 | 100% |
| 10 | D. Bertsekas, Dynamic Programming and Optimal Control (2nd ed.), Ath… (2000) | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 54 scored citations.