Conference Paper

Optimal behavior of electric vehicle parking lots as demand response aggregation agents

Authors:
  • Portucalense University Infante D. Henrique
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Abstract

With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, Plug-in Electric Vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based Demand Response Programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.

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... Numerous studies [25][26][27] focus on V2G, mainly EV charging management schemes at parking lots. The autonomous electric vehicle parking coordination system has been proposed to use electric vehicle batteries for supporting several V2G services. ...
... 25 The reduction of electricity cost and increasing profits for aggregators at parking lots have been considered. [26][27][28][29][30][31] The EV parking allocation and the EV charging impact on grid are assessed based on voltage violation, total system loss, and maximum system load. However, renewable energy sources (RESs) does not penetrate the grid; thus the effectual use of RES supported with EV management is not disputed. ...
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The work was conducted with the aim of finding a general method for solving the unit commitment (UC) problem. The proposed algorithm employs the evolutionary programming (EP) technique in which populations of contending solutions are evolved through random changes, competition, and selection. In the subject algorithm an overall UC schedule is coded as a string of symbols and viewed as a candidate for reproduction. Initial populations of such candidates are randomly produced to form the basis of subsequent generations. The practical implementation of this procedure yielded satisfactory results when the EP-based algorithm was tested on a reported UC problem previously addressed by some existing techniques such as Lagrange relaxation (LR), dynamic programming (DP), and genetic algorithms (GAs). Numerical results for systems of up to 100 units are given and commented on
Vehicle-to-grid (V2G) power flow regulations and building codes review by the AVTA
  • A Briones
A. Briones et al., "Vehicle-to-grid (V2G) power flow regulations and building codes review by the AVTA," Idaho Nat. Lab., U.S. Dept. Energy, Idaho Falls, ID, USA, Tech. Rep. INL/EXT-12-26853, 2012. [Online].