Abstract
Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the other hand, the use of the decentralized approach leads to an increase in the operation cost due to local optimization. In this paper, both centralized and decentralized approaches are combined for managing the operation of a distributed system, which is comprised of an MG and a community battery storage system (CBESS). The MG is formed by grouping all entities having the same operation objective and is operated under a centralized controller, i.e., a microgrid EMS (MG-EMS). The CBESS is operated by using its local controller with different operation objectives. A Q-learning-based operation strategy is proposed for optimal operation of CBESS in both grid-connected and islanded modes. The objective of CBESS in the grid-connected mode is to maximize its profit while the objective of CBESS in islanded mode is to minimize the load shedding amount in the entire system by cooperating with the MG. A comparison between the Q-learning-based strategy and a conventional centralized-based strategy is presented to show the effectiveness of the proposed strategy. In addition, an adjusted epsilon is also introduced for epsilon-greedy policy to reduce the learning time and improve the operation results.
Highlights
Microgrid (MG) is a small-scale electric power system, which can be operated both in islanded and grid-connected modes
The MG is interconnected with a community battery storage system (CBESS) and the utility grid
The Q-learning-based model for CBESS is implemented implemented in Python
Summary
Microgrid (MG) is a small-scale electric power system, which can be operated both in islanded and grid-connected modes. Most of the existing Q-learning-based operation methods have been developed for optimal operation of an agent in the grid-connected mode only for a particular objective, i.e., maximization of profit (competitive model). The power transfers between other community entities and among MGs of the network have not been considered in the existing Q-learning-based operation methods [19,20,21,22,23]. Q-learning-based optimization method, the operation results of the proposed method are compared simplified single MGs, cannot beEMS applied for networked. Simulation results have proved that the an adjusted epsilon method is applied in the epsilon-greedy policy to reduce the learning time proposed method can get similar results with the centralized EMS results, despite being a and improve the operation results. An adjusted epsilon method is applied in the epsilon-greedy policy to reduce the learning time and improve the operation results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
