Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids

• URL: http://arxiv.org/abs/2002.02581v2
• Date: Mon, 16 Nov 2020 15:50:04 GMT
• Title: Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids
• Authors: Lei Lei, Yue Tan, Glenn Dahlenburg, Wei Xiang, Kan Zheng
• Abstract summary: Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs. Two novel DRL algorithms are proposed to derive energy dispatch policies with and without fully observable state information.
• Score: 8.623472323825556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated respectively.

Related papers

• Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning [10.924928763380624]
  We investigate the scheduling issue of diesel generators (DGs) in an Internet of Things-Driven microgrid (MG) by deep reinforcement learning (DRL) The DRL agent learns an optimal policy from history renewable and load data of previous days. The goal is to reduce operating cost on the premise of ensuring supply-demand balance.
  arXiv  Detail & Related papers  (2023-04-28T23:52:50Z)
• Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach [66.53364438507208]
  An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated. Non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH) An advanced LSTM based algorithm is developed to predict users' dynamic communication state. A DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix RIS.
  arXiv  Detail & Related papers  (2023-04-11T13:16:28Z)
• Energy Management of Multi-mode Plug-in Hybrid Electric Vehicle using Multi-agent Deep Reinforcement Learning [6.519522573636577]
  Multi-mode plug-in hybrid electric vehicle (PHEV) technology is one of the pathways making contributions to decarbonization. This paper studies a multi-agent deep reinforcement learning (MADRL) control method for energy management of the multi-mode PHEV. Using the unified DDPG settings and a relevance ratio of 0.2, the proposed MADRL system can save up to 4% energy compared to the single-agent learning system and up to 23.54% energy compared to the conventional rule-based system.
  arXiv  Detail & Related papers  (2023-03-16T21:31:55Z)
• Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning [0.0]
  We propose a sophisticated deep reinforcement learning (DRL) methodology with a policy-based algorithm to plan energy storage systems (ESS) A quantitative performance comparison proved that the DRL agent outperforms the scenario-based optimization (SO) algorithm. The corresponding results confirmed that the DRL agent learns the way like what a human expert would do, suggesting reliable application of the proposed methodology.
  arXiv  Detail & Related papers  (2022-12-12T02:24:50Z)
• Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning [6.708717040312532]
  In this paper, deep reinforcement learning (DRL) is applied to learn an optimal policy for making joint energy dispatch (ED) and unit commitment (UC) decisions in an isolated microgrid. We propose a DRL algorithm, i.e., the hybrid action finite-horizon DDPG (HAFH-DDPG), that seamlessly integrates two classical DRL algorithms. A diesel generator (DG) selection strategy is presented to support a simplified action space for reducing the computation complexity of this algorithm.
  arXiv  Detail & Related papers  (2022-06-03T16:22:03Z)
• Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [76.96698721128406]
  Mobile edge computing (MEC) considered a novel paradigm for computation and delay-sensitive tasks in fifth generation (5G) networks and beyond. This paper provides a comprehensive research review on free-enabled RL and offers insight for development.
  arXiv  Detail & Related papers  (2022-01-27T10:02:54Z)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• Learning Centric Power Allocation for Edge Intelligence [84.16832516799289]
  Edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.
  arXiv  Detail & Related papers  (2020-07-21T07:02:07Z)
• Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
  We study a risk-aware energy scheduling problem for a microgrid-powered MEC network. We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
  arXiv  Detail & Related papers  (2020-02-21T02:14:38Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.