An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids
Authored by Kaveh Dehghanpour, Hashem Nehrir
Date Published: 2019
DOI: 10.1109/tsg.2017.2746014
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Abstract
In this paper, we propose an agent-based hierarchical power management
model in a power distribution system composed of several microgrids
(MGs). At the lower level of the model, multiple MGs bargain with each
other to cooperatively obtain a fair, and Pareto-optimal solution to
their power management problem, employing the concept of Nash bargaining
solution and using a distributed optimization framework. At the highest
level of the model, a distribution system power supplier, e.g., a
utility company, interacts with both the cluster of the MGs and the
wholesale market. The goal of the utility company is to facilitate power
exchange between the regional distribution network consisting of
multiple MGs and the wholesale market to achieve its own private goals.
The power exchange is controlled through dynamic energy pricing at the
distribution level, at the day-ahead and real-time stages. To implement
energy pricing at the utility company level, an iterative machine
learning mechanism is employed, where the utility company develops a
price-sensitivity model of the aggregate response of the MGs to the
retail price signal through a learning process. This learned model is
then used to perform optimal energy pricing. To verify its
applicability, the proposed decision model is tested on a system with
multiple MGs, with each MG having different load/generation data.
Tags
Optimization
System
Penetration
Agent-based
modeling
Microgrids
Bargaining games
Distributed optimization
Power management