Strategic Bidding of Hybrid AC/DC Microgrid Embedded Energy Hubs: A Two-Stage Chance Constrained Stochastic Programming Approach

TLDR: Results have verified the effectiveness of the proposed method, providing efficient bidding curves to the EHO through the stochastic management, and shows that the proposed strategy can balance the operational cost and service quality via the adjustment of chance constraints.

Abstract: To realize high efficient energy conversion among multiple energy carriers in market environment, the hybrid ac/dc microgrid is embedded as the electrical hub for energy hubs (EHs), and a stochastic day-ahead bidding strategy is proposed for the energy hub operators (EHOs). The electricity, heating, and cooling are managed to balance the operational cost and thermal service quality, while participating into the day-ahead market and real-time market. The uncertainties of prices, electrical loads, and ambient temperature are depicted by scenario trees, and are managed by a two-stage stochastic optimization scheme to minimize the expected and conditional value at the risk of operational cost. This stochastic optimization problem is reformulated to a linear programming (LP) problem under given conditions. In addition, a chance constraint is proposed to relax the quality of thermal services, and a two-stage chance constrained stochastic programming is formulated accordingly. It is further reformulated to a mixed integer LP problem. Simulations have been carried out on an EH with multiple types of energy generation, conversion, and storage systems. Results have verified the effectiveness of the proposed method, providing efficient bidding curves to the EHO through the stochastic management. Sensitive analysis shows that the proposed strategy can balance the operational cost and service quality via the adjustment of chance constraints.