Abstract
A hierarchical approach for the energy management of geographically close microgrids connected through a dedicated AC power network is proposed in this paper. The proposed approach consists of a two-layer energy management system (EMS) for networked microgrids. In the lower layer, each microgrid solves its own economic dispatch problem through a distributed model predictive control approach that respects capacity limits and ramp-rate constraints of distributed generation. In the upper layer, the energy trading in the network of microgrids decides how to optimally trade the energy based on the marginal cost information from the lower layer in order to improve global optimization objectives, e.g., social welfare. In order to solve the trading problem, a consensus-based algorithm and a replicator dynamics algorithm are proposed assuming that the marginal cost function of the microgrid is known and linear. It is shown that both algorithms converge to the same solution, which is equivalent to the minimization of operation costs. The consensus-based algorithm is extended in order to tackle more general marginal cost functions and trading network constraints. Moreover, the effect of ramp constraints and network limits is studied. Simulations show the effectiveness of the proposed algorithms for three interconnected microgrids with different characteristics.
Highlights
The growing development of more efficient renewable generation technologies and storage systems, has shifted the energy generation paradigm towards decentralization
We propose a hierarchical approach for the energy trading in networked MGs (NMGs) that combines a hybrid high-level layer with a distributed low-level layer
In the power systems that operate under a single competitive market, there is a unique price related to marginal costs of generators
Summary
The growing development of more efficient renewable generation technologies and storage systems, has shifted the energy generation paradigm towards decentralization. Research efforts have focused on integrating the resource management with mechanisms of incentives in order to obtain a full trading mechanism for MGs. Several works about trading in NMGs have focused their efforts on MGs that are interconnected through a dedicated AC distribution power network and are connected with the utility grid. The authors consider the interaction among buyers through an evolutionary game approach where they cooperatively select sellers and the proportion of energy to be acquired The core of these works is to show that efficient trading in MGs consist of the two problems mentioned before, i.e, management and incentives. In [15], the authors propose a three-layer hierarchical market scheme for the operation of multiple MGs. The first and secondary levels (day-ahead, hour-ahead) of the market solve the energy management problem from a game-theoretic framework through a double auction scheme, where the resource dispatch is determined after market clearing.
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