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Smart city article details
| Title | Optimal Multi-Market Scheduling Of A Sustainable Photovoltaic-Oriented Distribution Network Hosting Hydrogen Vehicles And Energy Storages: A Regret Assessment Optimization |
|---|---|
| ID_Doc | 40434 |
| Authors | Li J.; Wang W.; Yuan Z.; Chen J.; Xu L. |
| Year | 2023 |
| Published | Journal of Energy Storage, 66 |
| DOI | http://dx.doi.org/10.1016/j.est.2023.107489 |
| Abstract | One of the goals of the smart-grids is to increase the penetration of renewable energy sources like photovoltaics (PVs) and hydrogen-based resources in a controllable manner. To fully utilize these resources in smart cities, dealing with a significant amount of generation and demand uncertainty may be necessary. This article proposed a two-stage optimization procedure smart distribution grid at the city level with a high penetration of solar and hydrogen-powered cars considering the real-time and day-ahead markets. In addition to renewable resources, the proposed network also considers energy storage (ESs), generator units (GUs), and demand response programs (DRPs). In this paper, uncertainties involved in the system are modeled using a scenario-based method. Then, a regression index, so-called maximum relative regret (MRR) derived from the stochastic p-robust optimization (SPRO) method, is utilized to model the imposed regret on uncertainties. Uncertainties in the proposed system include PV generation, electricity and hydrogen demands, real-time market prices, and hydrogen-powered car behavior. The IEEE 33 bus test system has chosen to evaluate the proposed regret-based framework. The findings show that the proposed approach is well-suited to the conservative SPRO technique for the employed system, in which the projected cost of the objective function increases by just 3.40 % for a 45.42 % decrease in the MRR. © 2023 Elsevier Ltd |
| Author Keywords | Energy storage; Hydrogen vehicles; PV-oriented smart grid; Regret modeling; Stochastic p-robust optimization; Sustainable distribution network |
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