Stochastic multi-objective optimization to design optimal transactive pricing for dynamic demand response programs: A bi-level fuzzy approach

Abstract This paper proposes a stochastic multi-objective optimization framework to design real-time pricing for transactive energy. The energy transaction among the load-serving entity and customers is formulated as a bi-level optimization in which load-serving entity acts as the leader in the upper-level of optimization aiming to profit and reserve maximization. To make the interaction between opposite objectives, the modified fuzzy method is employed. The load-serving entity takes part in the wholesale market and purchases energy from the wholesale market and sells it to the customers. The load-serving entity itself possesses some dispatchable resources and it can also buy energy from privately-owned renewable resources. On the other hand, demand response providers are buying energy from the load-serving entity on behalf of consumers. The demand response providers are the followers of the proposed model and seek to maximize the profit of their customers. The communication between LSE and customers can be provided by the energy internet. Using the energy internet, the LSE controls and monitors the behavior of costumers to design real-time prices. The proposed model is tested on a standard case study, and the results show that the reservation and energy not supplied have been improved as 12% and 43.8%, respectively.