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The proliferation of small-scale renewable generators and price-responsiveloads makes it a challenge for distribution network operators (DNOs) toschedule the controllable loads of the load aggregators and the generation ofthe generators in real-time. Additionally, the high computational burden andviolation of the entities' (i.e., load aggregators' and generators') privacymake a centralized framework impractical. In this paper, we propose adecentralized energy trading algorithm that can be executed by the entities ina real-time fashion. To address the privacy issues, the DNO provides theentities with proper control signals using the Lagrange relaxation technique tomotivate them towards an operating point with maximum profit for entities. Todeal with uncertainty issues, we propose a probabilistic load model and robustframework for renewable generation. The performance of the proposed algorithmis evaluated on an IEEE 123-node test feeder. When compared with a benchmark ofnot performing load management for the aggregators, the proposed algorithmbenefits both the load aggregators and generators by increasing their profit by17.8%and 10.3%, respectively. When compared with a centralized approach, ouralgorithm converges to the solution of the DNO's centralized problem with asignificantly lower running time in 50 iterations per time slot.

A Decentralized Framework for Real-Time Energy Trading in Distribution Networks with Load and Generation Uncertainty