832 research outputs found
Review of the State-of-the-Art on Adaptive Protection for Microgrids based on Communications
The dominance of distributed energy resources in microgrids and the
associated weather dependency require flexible protection. They include devices
capable of adapting their protective settings as a reaction to (potential)
changes in system state. Communication technologies have a key role in this
system since the reactions of the adaptive devices shall be coordinated. This
coordination imposes strict requirements: communications must be available and
ultra-reliable with bounded latency in the order of milliseconds. This paper
reviews the state-of-the-art in the field and provides a thorough analysis of
the main related communication technologies and optimization techniques. We
also present our perspective on the future of communication deployments in
microgrids, indicating the viability of 5G wireless systems and
multi-connectivity to enable adaptive protection.Comment: Accepted to IEEE Trans. on Industrial Informatic
A novel distributed privacy-preserving control and data collection method for IoT-centric microgrids
Abstract The privacy of electricity consumers has become one of the most critical subjects in designing smart meters and their proliferation. In this work, a multilayer architecture has been proposed for anonymous data collection from smart meters, which provides: (1) The anonymity of information for thirdâparty data consumers; (2) Secure communication to utility provider network for billing purposes; (3) Online control of data sharing for endâusers; (4) Low communication costs based on available Internet of things (IoT) communication protocols. The core elements of this architecture are, first, the digital twin equivalent of the cyberâphysical system and, second, the Tangle distributed ledger network with IOTA cryptocurrency. In this architecture, digital twin models are updated in realâtime by information received from trusted nodes of the Tangle distributed network anonymously. A smallâscale laboratory prototype based on this architecture has been developed using the dSPACE SCALEXIO realâtime simulator and openâsource software tools to prove the feasibility of the proposed solution. The numerical results confirm that after a few seconds of anomaly detection, the microgrid was fully stabilized around its operating point with less than 5% deviation during the transition time
Deep Reinforcement Learning for Control of Microgrids: A Review
A microgrid is widely accepted as a prominent solution to enhance resilience and performance in distributed power systems. Microgrids are flexible for adding distributed energy resources in the ecosystem of the electrical networks. Control techniques are used to synchronize distributed energy resources (DERs) due to their turbulent nature. DERs including alternating current, direct current and hybrid load with storage systems have been used in microgrids quite frequently due to which controlling the flow of energy in microgrids have been complex task with traditional control approaches. Distributed as well central approach to apply control algorithms is well-known methods to regulate frequency and voltage in microgrids. Recently techniques based of artificial intelligence are being applied for the problems that arise in operation and control of latest generation microgrids and smart grids. Such techniques are categorized in machine learning and deep learning in broader terms. The objective of this research is to survey the latest strategies of control in microgrids using the deep reinforcement learning approach (DRL). Other techniques of artificial intelligence had already been reviewed extensively but the use of DRL has increased in the past couple of years. To bridge the gap for the researchers, this survey paper is being presented with a focus on only Microgrids control DRL techniques for voltage control and frequency regulation with distributed, cooperative and multi agent approaches are presented in this research
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