Optimal Pricing and Energy Scheduling for Hybrid Energy Trading Market in Future Smart Grid

TLDR

Future smart grid connects consumers to both traditional utilities and local energy networks, enabling bidirectional trading and cooperative local markets that benefit consumers and distributed sellers. The study investigates a hybrid energy trading market combining an external utility and a local trading center. The authors quantify consumer and seller benefits, model two LTC types (nonprofit‑oriented and profit‑oriented), formulate optimal trading problems, and propose algorithms to compute optimal LTC prices and energy schedules. Numerical results validate the benefits of the hybrid market and the performance of the proposed algorithms.

Abstract

Future smart grid (SG) has been considered a complex and advanced power system, where energy consumers are connected not only to the traditional energy retailers (e.g., the utility companies), but also to some local energy networks for bidirectional energy trading opportunities. This paper aims to investigate a hybrid energy trading market that is comprised of an external utility company and a local trading market managed by a local trading center (LTC). The existence of local energy market provides new opportunities for the energy consumers and the distributed energy sellers to perform the local energy trading in a cooperative manner such that they all can benefit. This paper first quantifies the respective benefits of the energy consumers and the sellers from the local trading and then investigates how they can optimize their benefits by controlling their energy scheduling in response to the LTC's pricing. Two different types of the LTC are considered: 1) the nonprofit-oriented LTC, which solely aims at benefiting the energy consumers and the sellers; and 2) the profit-oriented LTC, which aims at maximizing its own profit while guaranteeing the required benefit for each consumer and seller. For each type of the LTC, the optimal trading problem is formulated and the associated algorithm is further proposed to efficiently find the LTC's optimal price, as well as the optimal energy scheduling for each consumer and seller. Numerical results are provided to validate the benefits of the hybrid energy trading market and the performance of the proposed algorithms.