Optimal scheduling management of the parking lot and decentralized charging of electric vehicles based on Mean Field Game

• As an intermediary for the interaction between the grid and electric vehicles (EVs), the parking lot aggregator not only facilitates the exchange of electricity between EVs and the grid, but also brings benefits to all participants. This paper proposes a linear quadratic (LQ) Mean Field Game (MFG) theory with a major player to optimal scheduling management of the parking lot and formulate optimal decentralized charging control strategies for a large number of EVs, to achieve the minimization of EVs charging cost while maximizing the profit of the parking lot, but these two problems are a set of coupled control problems. In addition to modeling the interaction between the parking lot and the EVs as a finite-time dynamic game problem, the Nash Certainty Equivalence (NCE) of related optimization problems is also proposed, and the corresponding solution algorithm is designed. The control effects of the proposed dynamic game problem on the charging cycle, as well as the effects of parameters change and electricity price fluctuations on charging control are illustrated through numerical simulations.

Subject headings

NATURVETENSKAP  -- Matematik -- Beräkningsmatematik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Computational Mathematics (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Keyword

Electric vehicles

Mean Field Game

Optimal charging control

Nash Certainty Equivalence

Vehicle-to-grid

Publication and Content Type

art (subject category)

ref (subject category)