An electric vehicle dispatch module for demand-side energy participation

Abstract

The penetration of the electric vehicle (EV) has increased rapidly in recent years mainly as a consequence of advances in transport technology and power electronics and in response to global pressure to reduce carbon emissions and limit fossil fuel consumption. It is widely acknowledged that inappropriate provision and dispatch of EV charging can lead to negative impacts on power system infrastructure. This paper considers EV requirements and proposes a module which uses owner participation, through mobile phone apps and on-board diagnostics II (OBD-II), for scheduled vehicle charging. A multi-EV reference and single-EV real-time response (MRS2R) online algorithm is proposed to calculate the maximum and minimum adjustable limits of necessary capacity, which forms part of decision-making support in power system dispatch. The proposed EV dispatch module is evaluated in a case study and the influence of the mobile app, EV dispatch trending and commercial impact is explored.

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

“…Ω n is a T-dimensional vector, the column from t s n to t e n in Ω n is η n , the rest is 0. b d is a T-dimensional vector of coefficients. The right half of Equation (23) represents nonnegative variables of decision vector; it has the same form as Equation (19). Equation 20 is a linear function to be minimized and has the same form as Equation (17).…”

“…Equations (21) and (22) and the right half of Equation (23) are linear constraints and have the same structure as Equation (18). The right half of Equation (23) represents nonnegative variables of decision vector; it has the same form as Equation (19). From the above, the problem of minimizing carbon emissions in Section 2.1 belongs to LP.…”

“…Generally, most of these centralized scheduling models are NP-hard or nonconvex. 19 In order to prepare for a faster optimal solution, it is necessary to analyze the computational performance of the conventional optimization algorithms. The heuristic algorithms are widely used in the power grid optimization to significantly improve the grid performance.…”

“…Especially in real-time charging scheduling of large-scale PEVs, the scheduling process must be executed in minutes or even some seconds to ensure the implementation of coordinated charging. 19 In order to prepare for a faster optimal solution, it is necessary to analyze the computational performance of the conventional optimization algorithms.…”

Automated Energy Scheduling Algorithms for Residential Demand Response Systems

“…In particular, the power demand is concentrated at a specific time, thus the frequency of power outages is also increasing. Smart grids are emerging as a new technology to solve such problems by electricity demand control or integration, sustainable mobility, and renewable energy sources; these technologies are essential [3,4]. Among smart grid technologies, demand response (DR) technology is attracting much attention for its ability to intelligently drive the usage of electricity [5][6][7][8].…”