Organización de la OCSR en el marco del PPMGE

Section 2.4 shows the comparison of GKDE and DKDE. While GKDE and DKDE have their strengths in predicting irregular and regular charging patterns, respectively, the nov- elty detection method exploits the synergy between them. Specifically, the novelty detection is utilized to determine charging pattern regularities, so that GKDE can be applied for irregular-pattern EV users while DKDE for regular patterns. Thus the proposed HKDE leads to more accurate predictions in comparison to using only either GKDE or DKDE.

tion 2.5 examines more machine learning prediction approaches and investigates EV charging behaviors aiming to find the best method for its prediction. It is found that, in general, pre- dicting SMAPEs positively correlate to data sparsity/entropy ratio (R) but this relationship for GKDE and DKDE is relatively weak. Therefore, the KDE method can be utilized to handle the high R data with lower prediction error. Based on this property and the analysis result, SVR, RF, and DKDE are selected to compose the EPA. The synergy of the three algorithms enhances the prediction performance where SVR is good at predicting EV stay duration, RF performs better on energy consumption estimation, and DKDE takes care of the prediction with the high R data. The estimations by HKDE and EPA are applied to the optimal EV charging scheduling algorithm for load variation and charging cost minimization in the next chapter.

CHAPTER 3

EV Charging Scheduling Model

3.1

Overview

The surge of EV has been observed in the past few years, and the global EV market continues to grow[MH14, SL19] because of the dwindling of the fossil fuel and the dedication to reducing carbon footprint emission worldwide. As in California, it is expected to have 1.5 million zero-emission EVs on the road by 2025 from the initiative from the government[Off12], and consequently, the increasing demand of the Electrical Vehicle Supply Equipment (EVSE) is foreseen. While the number of EV is increasing, without proper charging management, the uncoordinated power consumption on a local scale can stress the electrical grid and lead to grid problems that degrading power quality and reliability[CHD09, MWJ14, SIF15]. Therefore, more and more studies are focusing on EV charging coordination in order to accommodate the increasing number of EVs.

It is still a challenging task to manage a massive number of EV charging. First of

all, there are several uncertainties on the demand side, such as start charging time, stay duration, and energy demand, as discussed in Section 2. Secondly, an uncoordinated EV charging may degrade the grid power quality or even damage the grid because it can produce a huge power demand that exceeds the grid capacity [LSA10]. Thirdly, the integration of renewable energy resources such as photovoltaic (PV) panel, requires proper control method for optimal energy utilization and PV intermittency alleviation [Cal16]. Lastly, the dynamic electricity price may significantly affect the EV charging cost. All of the above should be taken into consideration for an effective, real-time EV charging scheduling system.

3.2

Literature Review

A considerable number of studies have been made on EV charging management not only in the aspect of an economical implementation of EVSE but also in the reliability of a distri- bution grid, which is to alleviate the deteriorating impact of uncoordinated EV charging. [DTB12] and [LWL12] discuss the optimal sizing and location of EVSE while [LGZ18] further demonstrates the need of multi-types of charging facilities for optimal EVSE deployment. [TMN17] presents a charging scheduling algorithm to accommodate a high penetration of EVs and DERs. Also, [HC16] discusses EV charging scheduling with the consideration of vehicle-to-grid (V2G) capability. Studies of EV load scheduling fall into two approaches: centralized and distributed methods. Centralized means a central entity (CE) directly con- trols the EV; namely, a CE solves the optimization problem and broadcasts the results to the EVs [CHD09, BNE16]. The objectives of the optimization includes minimizing power loss [CHD09, SHM10], regulating load factor [SHM10] or maximizing supportable EV penetra- tion [LSA10]. The centralized infrastructure requires to collect the information form all EVs and centrally optimize their charging schedules. Therefore, EV owners’ privacy becomes an issue. Also, when EV penetration increases, the data is more difficult to manage, and the curse of dimensionality becomes a problem. On the other hand, a distributed approach is more suitable for managing a large scale of EV charging. In this method, CE coordinates the EV load demand through communication with the EV chargers [LBM16]. That is, in- stead of solving the scheduling problem, including many variables centrally, it is solved in a distributed and iterative manner between CE and EV charging agents. Over the past few years, a large number of articles have been devoted to the study of distributed EV charg- ing scheduling approach. What seems to be lacking, however, is to consider user behavior stochasticity. Load flattening is achieved in [CF10], but the EV stay duration, energy de- mand are assumed to be known. [GTL13, MCH11] have optimally scheduled EV charging to achieve load valley filling, but user behavior uncertainties are not considered. [MCH11] assumed every EV has the same charging window and the same charging demand, which is not realistic.

In the following section, an EV charging scheduling algorithm incorporated with the EV user behavior prediction method introduced in chapter2 is presented, with the objectives of minimizing load variance and reducing charging cost.