A Decentralized Framework for Real-Time Energy Trading in Distribution Networks with Load and Generation Uncertainty
For distribution network operators and energy entities, this provides a practical decentralized solution that addresses privacy and computational challenges in real-time trading under uncertainty.
The paper proposes a decentralized real-time energy trading algorithm for distribution networks that handles load and generation uncertainty while preserving entity privacy. Compared to a no-load-management benchmark, it increases load aggregator profit by 17.8% and generator profit by 10.3%, and converges to the centralized solution with lower running time.
The proliferation of small-scale renewable generators and price-responsive loads makes it a challenge for distribution network operators (DNOs) to schedule the controllable loads of the load aggregators and the generation of the generators in real-time. Additionally, the high computational burden and violation of the entities' (i.e., load aggregators' and generators') privacy make a centralized framework impractical. In this paper, we propose a decentralized energy trading algorithm that can be executed by the entities in a real-time fashion. To address the privacy issues, the DNO provides the entities with proper control signals using the Lagrange relaxation technique to motivate them towards an operating point with maximum profit for entities. To deal with uncertainty issues, we propose a probabilistic load model and robust framework for renewable generation. The performance of the proposed algorithm is evaluated on an IEEE 123-node test feeder. When compared with a benchmark of not performing load management for the aggregators, the proposed algorithm benefits both the load aggregators and generators by increasing their profit by 17.8%and 10.3%, respectively. When compared with a centralized approach, our algorithm converges to the solution of the DNO's centralized problem with a significantly lower running time in 50 iterations per time slot.