Practical closed-loop dynamic pricing in smart grid for supply and demand balancing

Abstract Pricing strategy for power systems is an important and challenging problem, due to the difficulties in predicting the demand and the reactions of customers to the price accurately. Any prediction errors may result in higher costs to the supplier. To address this issue, in this paper, we propose a novel, practical closed-loop pricing algorithm (PCPA). Using the closed-loop control to well coordinate the customers and the supplier, the power system can run more efficiently, resulting in both cost saving for customers and higher profit for the supplier. We prove the convergence of PCPA, i.e., a stable price can be achieved. We provide sufficient conditions to guarantee the win-win solution for both the customers and the supplier, and an upper bound of the gain. We also provide a necessary and sufficient condition of that the highest win for both the customers and the supplier can be achieved. Extensive simulations have shown that PCPA can outperform the existing prediction-based pricing algorithms. It shows that the profit gain of the proposed algorithm can up to 100% when the total demand can be fixed to the optimal demand.