Advancements in the field of Information and Communication Technologies (ICT) has enabled the possibility to utilize the flexibility offered by responsive assets in a better way by employing Demand Response (DR) schemes. This thesis analyzes the performance of one such DR scheme developed at TU Delft called Forecast mediated Market Based Control (F-MBC), which aims to coordinate such flexible assets by communicating "self-fulfilling forecasts" [1]. The main aim of the project is to investigate the applicability of this method in real-world settings. To do so, several simulation scenarios were formulated to understand how well F-MBC coordinates heterogeneous populations of uninterruptible time shiftable loads over an extended time horizon, both from the system perspective and devices' perspective. The thesis also proposes an approach to test the mechanism in a rolling horizon setup. First, the performance of F-MBC is examined under several combinations of deferrable loads having identical deadlines. Then, its ability to coordinate devices with dynamic load profiles under a complex realistic setting is investigated. Trade-offs adopted when simulating such a setup is also highlighted. Results indicate that while F-MBC achieves good overall performance when coordinating devices with uniform power consumption profiles, its performance in scheduling heterogeneous populations of devices with dynamic load profiles was quite variable. When devices that consume high power when they start was considered for coordination, F-MBC was able found to allocate the devices in such a manner that steered towards overall cost minimization. However, its performance if used to schedule devices which consume low power when it starts was found to be undesirable. Hence, several recommendations were provided to deduce better conclusions about the applicability of the mechanism in reality. References: [1]Hazem A. Abdelghany, Simon H. Tindemans, Mathijs M. de Weerdt, Han la Poutré, Distributed coordination of deferrable loads: A real-time market with self-fulfilling forecasts, Sustainable Energy, Grids and Networks, Volume 23, 2020, 100364, ISSN 2352-4677. http://www.sciencedirect.com/science/article/pii/S2352467720302952