The results obtained from the TSR model, have shown the ability of each battery capacity to fulfill its daily trips with different FCS allocations. It is observed that PEVs with a battery capacity of 16kWh showed huge dependence on the charging station network for highway trips. However, about 97% of all highway trips are completed successfully in the absence of FCSs if all PEVs’ batteries are 54kWh and above. Another important observation from the TSR model results is that PEV battery capacities influence FCS service range, and therefore, considering the data from PEV market sales in selecting optimum FCS sites leads to more realistic and accurate outcomes.
The proposed model has been applied to different scenarios for two types of network: In-city and Highway. The results show clearly the robustness of the proposed model, and the outcomes of the model demonstrate the significance and the advantage of the proposed model when compared to the models reported in the literature. It is also observed that the number of FCSs in-city is very sensitive to the charging station service range (CSSR) due to the quadratic relationship between service range and the covered area. In the highway scenario, the CSSR should be shorter in distance than the segments between any two neighboring FCSs; otherwise, the TSR level should be reduced in order to get an appropriate CSSR.
4.6 Chapter assessment
In this chapter, a new PEV charging station allocation model has been presented. The model consists of two parts. In the first part, the relationship between charging station service range and the probability of PEVs completing trips successfully is discussed. The model utilizes an MCS to generate virtual trip distances and PEVs’ remaining electric ranges. It takes into consideration the variations in driving habits, the battery capacities, the states of charge, and the trip classes. Consideration of the variations in these
factors is assumed to present a more realistic and accurate model for estimating the trip success ratio for each charging station service range as compared to the literature.
In the second part, different CSSRs are utilized in the allocation optimization problem in order to locate the charging stations in the optimal locations in order to assure that the TSR of PEVs is above a certain threshold. Instead of using a single service range or Origin-Destination (OD) pair path, the model locates the FCSs using different CSSRs by applying a maximum coverage location problem (MCLP). The results obtained show the differences in quality of service based on their TSR levels. Therefore, the proposed model is capable to measure how successful the FCS network is in meeting PEV demand in order to make the optimum decisions based on the available resources. Moreover, the proposed model considers PEV accessibility in the location problem by using TSR levels, so the model outcomes are influenced by drivers’ needs rather than electrical utilities’ requirements.
The traffic volume data in addition to the forecasted penetration level of PEVs (Chapter 3) will produce the estimated charging demand of PEVs in the next chapter. This demand will be distributed over the transportation network at the selected optimal locations presented in this chapter. The target locations will be utilized in the next chapter as candidate locations for the decoupled network (transportation network and distribution system) in order to transfer PEV demand from the transportation network to the distribution system.
Technical Evaluation for Accommodating PEV Load in Distribution
System
Accommodating a penetration of PEV charging has been dealt with in the literature only with regard to either normal charging (Level 1 and Level 2), as in [39, 40, 43 – 45], or fast charging (Level 3), as in [35, 37, 41]. However, considering both normal and fast charging levels when investigating the accommodation of PEVs, was not discussed in a great depth. Obvious gaps exist between the solutions proposed in the literature and the status of the current grid, which can be summarized as follows:
The absence of PEV public charging data (Level 3) presents a problem. The work presented in the area of estimating PEV charging demand must be enhanced using additional data that reflect charging characteristics and driver behaviors, but this information will not be available prior to significant PEV penetration levels and constructing charging station network.
There is a lack of evaluation and assessment of the additional electrical system requirements on low PEV penetration levels. With only a few exceptions, the ability of existing electrical systems to feed the additional PEV charging station load in the early adoption stage has not been investigated thoroughly in the previous work in this area.
Using public charging infrastructure is an essential need for PEV drivers; hence, the impact of using public charging infrastructure on distribution system Load Duration Curves (LDCs) has to be investigated in order to evaluate the ability of current distribution systems to serve the additional PEV loads.
The presented work in this chapter was thus undertaken with the goal of filling these gaps through the proposal of a technical evaluation algorithm based on Optimal Power Flow (OPF) as a means of assessing the ability of current distribution systems to serve PEV penetration levels in the early adoption stage. The results of this work are therefore expected to provide an alternative for upgrading the distribution system during the transitional period between the current status of the grid and a significant penetration of PEVs. The additional load from PEVs will be matched only with the required public charging infrastructure capacity.
5.1 Problem description
One of the major questions faced by electric utilities currently is whether the existing distribution network infrastructure would be able to serve a mass introduction of PEVs. In addition, if the existing distribution networks are not capable to do that, what are the necessary network requirements and reinforcement? PEVs have indeterminate penetration in electric grids due to uncertainties in charging and discharging
patterns. This uncertainty, together with variations in driving habits, makes it difficult to evaluate accurately the impacts on local distribution networks. The uncoordinated and random charging activities of PEVs could significantly stress the distribution system, causing:
Degraded system efficiency
Severe voltage fluctuations and violations
Increased probability of outages due to network local overloads
Furthermore, the charging levels of different PEVs would disrupt the distribution grid to some extent. Therefore, the planners should evaluate the maximum possible penetration of PEVs in order to maintain seamless operation of the present network without violating its technical constraints.
In this chapter, the proposed technical evaluation algorithm is described, including modeling PEV loads at residential and public locations. The input for the proposed algorithm comprises the normal load model, the PEV uncoordinated residential charging model, and the PEV public charging model. The output of the proposed algorithm consists of the size of candidate FCSs for the selected locations (Chapter 4) as well as the target PEV penetration and its public share of charging. The proposed technical evaluation is intended to demonstrate the impact of charging some of the PEV from public charging networks rather than considering only residential charging option and the effect of this new trend on the system electric demand. It is also intended to investigate how much PEV public charging percentages (shares) using FCSs can affect the ability of the existing distribution system to serve and adopt PEV demand without any technical violations.