This article presents a controller hardware-in-the-loop (CHIL) validation of a novel energy storage management (ESM) solution designed to optimize the use of grid-connected distributed energy resources (DERs) based on the forecasting of generation, load, and real-time energy prices. The proposed control strategy aims to control the operations of the energy storage (ES) using the A* searching algorithm so that the total cost of energy used to serve the local electrical load is minimized. The proposed solution uses the energy storage states and available forecast data to generate a graph that is explored using the A* search algorithm with the objective of finding the optimum cost of energy. The proposed ESM is tested offline against genetic algorithm and sequential quadratic programming based solutions and shows cost improvement of 5% to 8.9%. A CHIL validation testbed was also implemented to evaluate the performance of the algorithm. A Florida based distribution grid was simulated using real field data in a DTRS, and the IEEE 1815 DNP3 communication protocol was used to establish the communication between the controller and the simulation. The graph search-based ESM was implemented in python in an external computer which served as the controller. Offline and real-time simulations are presented showing comparable results.