Plug-in electric buses (PEBs) are a promising alternative to conventional buses to provide a sustainable, economical, and efficient mode of transportation. However, electrification of public transportation leads to a phenomenon of peak load that impacts the stability of low voltage (LV) feeders. In this context, the effective integration of an energy storage system (ESS) and photovoltaic (PV) in a bus depot charging ecosystem can lead to i) peak load reduction and ii) charging cost reduction with low carbon emission. Therefore, a limited PEB charge scheduling algorithm is proposed for: i) bus depot operator (BDO) profit maximization and ii) grid stability enhancement considering the constraints of PEB charging and grids. A mixed integer linear programming (MILP) model for BDO profit maximization has been formulated and analyzed using IBM ILOG studio with CPLEX solver. Simulation has been performed for SkyBus electric fleet using real-world data such as actual bus arrival and departure schedules under diverse traffic, number of passengers, trip duration, daily load profile, solar radiation profile, and benchmark storage price. The charging impact of PEBs was tested on one of the distribution feeders in Auckland, New Zealand. The BDO generates revenue by performing energy trading among PV, ESS, PEBs, and buildings after incorporating capital investment, operation and maintenance, and depreciation costs.

A power distribution network is a critical infrastructure in any society and any disruption has an enormous impact on the economy and daily lives. Therefore, the objective of this study is to transform the conventional power distribution systems into resilient autonomous microgrid networks by optimally sizing and siting the distributed generators (DGs). First, N main DGs are placed to transform an existing network into an autonomous microgrid network. Second, all the possible combinations of the initially deployed DGs are made and then the outage of 1 to N − 1 DGs is considered. Considering the outage of DGs in each combination (one at a time), the resiliency of the network is analysed. Amount of load shedding, total power loss in the network, and voltage limits are analysed in this step. Finally, based on the resiliency analysis, additional DGs are placed to enhance the resiliency of the transformed network. Heuristic methods (particle swarm optimisation and genetic algorithm) are used for both sizing and siting of DGs during the first and the second steps. The objective of the formulation is to minimise load shedding, total power loss (active and reactive), and voltage deviations in the network during DG outages.