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Optimal placement of parking lots with optimal scheduling in power systems to maximize the benefit, considering different peak load states and different electricity prices for each state, is studied in this paper. The effectiveness of the proposed technique is tested on the IEEE 33-bus distribution test system with considering the power system constraints and using genetic algorithm. This algorithm selects the optimal sites and sizes (number of electric vehicles) of these parking lots and shows the optimal results. The results of these simulations with genetic algorithm demonstrate that the economic problem of parking lots placement depends on many items such as number of the electrical vehicles (EVs) in each parking lot (capacity of parking lot), state of charge of EVs, as well as the electricity price in peak/off-peak periods. Moreover, it is suggested that the existence of parking lots, considering adequate incentives for EVs owners, has economic benefit for distribution network operators, reduces total power loss, and can improve the distribution system voltage profile. Also, the number of parking lots to be allocated in a distribution system supports the distribution system at peak load hours because it is more interesting from economic point of view to install more parking lots.

Energy storage systems (ESSs) offer a set of unique benefits realizing the smart grid objectives. This paper contributes by establishing an optimal long-term integration of ESSs in these networks aiming at enhancing both the economic and technical metrics. Specifically speaking, the proposed approach deals with the following: (1) maximizing the power losses reduction benefit, (2) minimizing the operational costs, (3) minimizing the energy not supplied index, and (4) maximizing the benefits by upgrade deferral of distribution network. The established model is based on an AC load flow fashion satisfying all the technical and economical constraints. A probabilistic load model is adopted and incorporated in the proposed approach, to yield in a more realistic study. Both the size and the site of ESS installation are determined and assessed in the expansion horizon considering the yearly load growth rates. Two different scenarios are thoroughly assessed. In the first scenario, only 1 ESS unit is embedded in the network, and in the second view, the proposed approach is left to determine both the number and the size of installed ESSs. The numerical studies are conducted on IEEE 33-bus distribution test system within which the genetic algorithm performs as the optimization platform. The obtained results are discussed for economic and technical improvements. KEYWORDSan optimal long-term integration approach, distribution systems, economic and technical objectives, energy storage system (ESS)