In this paper, a real-time energy management algorithm (RTEMA) for a grid-connected charging park in an industrial/commercial workplace is developed. The charging park under study involves plug-in hybrid electric vehicles (PHEVs) with different sizes and battery ratings as well as a photovoltaic (PV) system. Statistical and forecasting models were developed as components in the developed RTEMA to model the various uncertainties involved such as the PV power, the PHEVs, arrival time, and the energy available in their batteries upon their arrival. The developed energy management algorithm aims at reducing the overall daily cost of charging the PHEVs, mitigating the impact of the charging park on the main grid, and contributing to shaving the peak of the load curve. Hence, the benefits of implementing this RTEMA is shared among the customers, the charging park considering all customers as a bulk of power connected to the grid, and the ac grid. This makes it applicable for various business models. The developed RTEMA utilizes a fuzzy controller to manage the random energy available in the PHEVs’ batteries arriving at the charging park and their charging/discharging times, power sharing among individual PHEVs that is commonly known as vehicle-to- vehicle functionality, and vehicle-to-grid service between the charging park and the main ac grid. The developed RTEMA was simulated using the standard IEEE 69-bus system at different penetration and distribution levels. The obtained results verify the effectiveness and validity of the developed RTEMA.