Incentivizing Energy Trading for Interconnected Microgrids
By Hao Wang, Jianwei Huang. IEEE Transactions on Smart Grid, 2016
466 citations recorded by the source corpus.
In this paper, we study the interactions among interconnected autonomous microgrids, and develop a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing. We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively. For practical implementation, we propose a decentralized solution method with minimum information exchange overhead. Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/447a3321050900d223ea55c3c7efa2bfef3aa501
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In Control and Systems Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Learning-Based Computation Offloading for IoT Devices With Energy Harvesting (2017, methodology): “The amount of energy harvested at time slot k can be estimated by the IoT device according to the power harvested history and the modeling method in [34].”