112 research outputs found
A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks
We consider the problem of energy management in microgrid networks. A
microgrid is capable of generating a limited amount of energy from a renewable
resource and is responsible for handling the demands of its dedicated
customers. Owing to the variable nature of renewable generation and the demands
of the customers, it becomes imperative that each microgrid optimally manages
its energy. This involves intelligently scheduling the demands at the customer
side, selling (when there is a surplus) and buying (when there is a deficit)
the power from its neighboring microgrids depending on its current and future
needs. Typically, the transaction of power among the microgrids happens at a
pre-decided price by the central grid. In this work, we formulate the problems
of demand and battery scheduling, energy trading and dynamic pricing (where we
allow the microgrids to decide the price of the transaction depending on their
current configuration of demand and renewable energy) in the framework of
stochastic games. Subsequently, we propose a novel approach that makes use of
independent learners Deep Q-learning algorithm to solve this problem. Through
extensive empirical evaluation, we show that our proposed framework is more
beneficial to the majority of the microgrids and we provide a detailed analysis
of the results
Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review
The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process
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