ABSTRACT
Contents 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 5.2 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.2.1 Research Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.2.2 Distribution Network Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.3 Electric Distribution Grid Optimizations. . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5.3.1 System Capabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5.3.2 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.3.2.1 Front End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 5.3.2.2 Back End . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 5.3.2.3 External Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.3.3 System Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 5.3.3.1 Electric Load Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 5.3.3.2 Simulation Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.3.3.3 Program Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 5.4.1 Assets Condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.4.1.1 Assets Condition on Different Networks . . . . . . . . . . 128 5.4.1.2 Assets Condition with Different Scenarios. . . . . . . . . 130
5.4.2 Load Increase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.4.3 EDN Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5.4.4 Transformer Conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
The expected widespread adoption of plug-in electric vehicles (PEVs) is likely to challenge the electric grid. The distribution infrastructure might suffer the most, as PEV charging can substantially alter the neighborhood load and put additional stress on local equipments, such as transformers and power lines. Distribution system operators (DSOs) therefore need to prepare for the impact on the electrical system. In this chapter, we present a novel web-based planning and optimization tool that simulates without a priori assumption the technical solutions needed to support the potential widespread use of PEVs. In particular, the tool describes the potential future times and points of failures on the electric distribution grid and provides a selection of optimization programs with associated costs. DSOs can choose the most relevant programs for their network and see the benefits of applying them. Different simulations using the tool show that vehicle charging will impact the grid in the intermediate and long term.
