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Performance of different optimal charging schemes in a solar charging station using dynamic programming
Author(s) -
Hajidavalloo Mohammad R.,
Shirazi Farzad A.,
Mahjoob Mohammad J.
Publication year - 2020
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2619
Subject(s) - photovoltaic system , grid , dynamic programming , minification , mathematical optimization , computer science , automotive engineering , computation , state of charge , scheduling (production processes) , total cost , power (physics) , solar power , electrical engineering , engineering , mathematics , algorithm , battery (electricity) , physics , geometry , quantum mechanics , economics , microeconomics
Summary Electric Vehicles (EVs) are gradually replacing conventional vehicles as they are environmentally friendly and cause less pollution problems. Unregulated charging has severe impacts on the distribution grid and may incur EV owners higher charging costs. Therefore, controlled charging infrastructures to supply the charging needs of large numbers of EVs are of vital importance. In this article, an optimal control scenario is presented to formulate the charge scheduling problem of EVs in a solar charging station (CS). Two different objective functions are considered. The first objective function holds for minimizing the total charging cost of EVs. In this case, the benefits of Vehicle‐to‐Grid (V2G) are investigated by comparing the charging costs of EVs with and without this capability. The total EV charging costs and grid benefits are also investigated in the second objective function which holds for minimizing the extracted power from the grid. A modified version of Dynamic Programming is used to solve the large state‐space model defined for the optimal control problem with extremely shorter computation time and minimal loss of optimality. Extensive simulations are done in two representative summer and winter climates to determine the role of solar energy in the CS performance. The results show that in the cost minimization algorithms, significant savings for EV owners and a smooth load shape for the grid are achieved. For the minimized power from the grid algorithm, a total near Photovoltaic (PV)‐curve charging power is obtained to exploit the PV power as much as possible to minimize the impacts on the grid.