Results 41 to 50 of about 13,256,052 (164)

Safe Deep Reinforcement Learning-Based Real-Time Multi-Energy Management in Combined Heat and Power Microgrids

IEEE Access
Combined heat and power microgrids (CHPMGs) have become increasingly popular recently due to their ability to offer cost-effective and resilient solutions to support critical infrastructures.
Bo Hu, Yuzhong Gong, Xiaodong Liang, C. Y. Chung, Bram F. Noble, Greg Poelzer   +5 more
doaj   +1 more source

Ethical Guardrail Bandit Pruning (EGBP): A Fairness‐Constrained Reinforcement Learning System for Equitable Governance of Distributed Bioenergy Grids

IET Renewable Power Generation, Volume 20, Issue 1, January/December 2026.
Distributed bioenergy networks face an unresolved equity challenge: AI dispatch optimisers under‐serve peripheral communities while maximising aggregate throughput. Ethical guardrail bandit pruning enforces fairness and energy sustainability via a two‐timescale primal‐dual guardrail, achieving a 76.6% Gini reduction and 28.5% FLOPs‐proxy reduction ...
Ntebogang Dinah Moroke
wiley   +1 more source

Risk-Aware Scheduling of Dual Criticality Job Systems Using Demand Distributions [PDF]

Leibniz Transactions on Embedded Systems, 2018
We pose the problem of scheduling Mixed Criticality (MC) job systems when there are only two criticality levels, Lo and Hi -referred to as Dual Criticality job systems- on a single processing platform, when job demands are probabilistic and their ...
Alahmad, Bader Naim, Gopalakrishnan, Sathish   +1 more
doaj   +1 more source

An Optimal Control Approach for Plug‐In Electric Vehicles in Active Distribution Systems Using Deep Reinforcement Learning

IET Smart Grid, Volume 9, Issue 1, January/December 2026.
Introduces a deep reinforcement learning (DRL)‐based control framework to manage plug‐in electric vehicles (PEVs) and distributed energy resources (DERs) in active distribution systems (ADSs). Integrates PEVs via solid‐state transformers (SSTs) to regulate reactive power and optimise both technical (power loss) and economic (operational cost ...
Yameena Tahir, Muhammad Faisal Nadeem, Muhammad Bilal Raza, Muhammad Akmal   +3 more
wiley   +1 more source

From Rational Agents to Learning Agents: A Structured Review and Conceptual Framework for MARL in Economic Modeling

Complexity, Volume 2026, Issue 1, 2026.
This paper provides a systematic review of the emerging literature on multiagent reinforcement learning (MARL) in economics. The review is motivated by the rapid expansion of MARL methods in computational modeling and the lack of a clear framework that connects this literature to established economic approaches.
Yiwei Wang, Hiroki Sayama
wiley   +1 more source

Coordinated Optimization of Active and Reactive Power of Active Distribution Network Based on Safety Reinforcement Learning

Zhongguo dianli
A safe reinforcement learning method based on offline strategies is proposed. Through offline training of a large amount of historical operating data of the distribution network, it gets rid of the traditional optimization method.
Hao JIAO, Yanyan YIN, Chen WU, Jian LIU, Chunlei XU, Xian XU, Guoqiang SUN   +6 more
doaj   +1 more source

A Real‐Time Scheduling Strategy for Virtual Power Plants Based on TCN‐SPPO

International Journal of Energy Research, Volume 2026, Issue 1, 2026.
The virtual power plant (VPP) provides a new model for the integration and management of distributed energy resources. This paper proposes a real‐time scheduling strategy for VPPs based on temporal convolutional network‐safe proximal policy optimization (TCN‐SPPO).
Qiang Xing, Qiming Yang, Mingshen Wang, Mingming Wu, Yiming Ni, Jiayu Wu, Fatemeh Boshagh   +6 more
wiley   +1 more source

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

Global Energy Interconnection
In this study, a novel residential virtual power plant (RVPP) scheduling method that leverages a gate recurrent unit (GRU)-integrated deep reinforcement learning (DRL) algorithm is proposed.
Xiaoyun Deng, Yongdong Chen, Dongchuan Fan, Youbo Liu, Chao Ma   +4 more
doaj   +1 more source

Intelligent Ascent Trajectory Optimization via Behavioral Cloning‐Augmented Reinforcement Learning From Demonstrations

International Journal of Aerospace Engineering, Volume 2026, Issue 1, 2026.
Traditional ascent trajectory optimization methods for hypersonic vehicles suffer from a high computational burden. This paper proposes an intelligent optimization algorithm based on deep reinforcement learning, where the offline‐trained neural network enables fast trajectory generation and online deployment while maintaining all terminal, control, and
Qingji Jiang, Xiaogang Wang, Bin E, Ningyu Wang, Chuang Liu   +4 more
wiley   +1 more source

Popularity-Aware Service Provisioning Framework in Cloud Environment

Applied Sciences
To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests.
Haneul Ko, Yumi Kim, Bokyeong Kim, Yeunwoong Kyung   +3 more
doaj   +1 more source