4.8 Article

Evolutionary Multi-Agent Deep Meta Reinforcement Learning Method for Swarm Intelligence Energy Management of Isolated Multi-Area Microgrid With Internet of Things

Related references

Note: Only part of the references are listed.
Article Chemistry, Physical

Optimal dual-model controller of solid oxide fuel cell output voltage using imitation distributed deep reinforcement learning

Jiawen Li et al.

Summary: To address the nonlinearity and constraints in solid oxide fuel cell (SOFC) control, a dual-model control framework (DMCF) is proposed, with a PID controller and a supplementary dynamic controller. The supplementary controller adapts to uncertainties and fuel utilization constraints, while an imitation distributed deep deterministic policy gradient (ID3PG) algorithm enhances the robustness and adaptive capacity. Simulation results demonstrate the effectiveness of the proposed framework in controlling SOFC output voltage and satisfying fuel utilization constraints.

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY (2023)

Article Automation & Control Systems

Distributed deep reinforcement learning-based gas supply system coordination management method for solid oxide fuel cell

Jiawen Li et al.

Summary: To maintain the net output power of solid oxide fuel cells (SOFC) and avoid violating oxygen excess ratio and fuel utilization constraints, a data-driven gas supply system coordination management method is proposed. The algorithm, called PE-MA4DPG, is based on population evolution and utilizes multi-agent double delay deep deterministic policy gradient. The algorithm's effectiveness is demonstrated in comparison to existing algorithms through three experiments.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2023)

Article Green & Sustainable Science & Technology

Data-driven cooperative load frequency control method for microgrids using effective exploration-distributed multi-agent deep reinforcement learning

Jiawen Li et al.

Summary: This study proposes a data-driven cooperative load frequency control method using a novel algorithm, which addresses the coordination control problem between the controller and power distributor through centralized training and decentralized execution, achieving a robust cooperative control strategy. The algorithm is verified in an LFC model of Zhuhai Tandang Island, showcasing its performance in an island microgrid setting.

IET RENEWABLE POWER GENERATION (2022)

Article Computer Science, Information Systems

Toward Future Internet of Things Experimentation and Evaluation

Thiago Bueno da Silva et al.

Summary: This article proposes a novel IoT experimentation and evaluation environment based on a low-cost and open-source solution. It integrates technologies of containers, IoT nodes emulation, and network simulation to evaluate the current IoT dual stack and a promising FIA approach called eXpressive Internet architecture. The experimental results demonstrate the feasibility and effectiveness of this approach.

IEEE INTERNET OF THINGS JOURNAL (2022)

Article Energy & Fuels

A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient

Jiawen Li

Summary: This paper proposes a data-driven multi-objective energy coordinative management policy to enhance the net output power and efficiency of a solid oxide fuel cell (SOFC). The policy focuses on maintaining stable oxygen excess ratio (OER) and fuel utilization (FU) ratio while meeting load demand through optimization agent and controller design.

APPLIED ENERGY (2022)

Article Computer Science, Artificial Intelligence

A Novel Automatic Generation Control Method Based on the Large-Scale Electric Vehicles and Wind Power Integration Into the Grid

Lei Xi et al.

Summary: This article proposes an improved reinforcement learning algorithm to solve the problem of frequency instability in power systems caused by large-scale electric vehicles and wind power grid connection. The algorithm expands the exploration space using an optimistic initialization principle and integrates double Q-learning to address the over-estimation issue. Simulation results demonstrate that the proposed algorithm obtains the global optimal solution and outperforms other reinforcement learning algorithms in terms of control performance.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Energy & Fuels

Efficient experience replay based deep deterministic policy gradient for AGC dispatch in integrated energy system

Jiawen Li et al.

Summary: A novel automatic generation control dispatch was proposed to balance stochastic power disturbance in integrated energy system, aiming to reduce control deviation and regulation mileage payment while improving training efficiency and action quality through multiple experience pool probability replay strategy. The proposed algorithm was verified on a two-area load frequency control model and Hainan province IES for different energy demand.

APPLIED ENERGY (2021)

Article Engineering, Electrical & Electronic

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

Jiajia Yang et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

Article Engineering, Electrical & Electronic

A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of a Multi-Area Power System

Ziming Yan et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2020)

Article Engineering, Electrical & Electronic

Robust Control Scheme for Distributed Battery Energy Storage Systems in Load Frequency Control

Arman Oshnoei et al.

IEEE TRANSACTIONS ON POWER SYSTEMS (2020)

Article Engineering, Electrical & Electronic

An Analytical Method for Generation Unit Aggregation in Virtual Power Plants

Ming Qu et al.

IEEE TRANSACTIONS ON SMART GRID (2020)

Article Engineering, Electrical & Electronic

Non-linear sliding mode control for frequency regulation with variable-speed wind turbine systems

Sheetla Prasad et al.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2019)

Article Automation & Control Systems

Distributed model predictive based secondary control for economic production and frequency regulation of MG

Faisal Mehmood et al.

IET CONTROL THEORY AND APPLICATIONS (2019)

Article Computer Science, Information Systems

NSAC: A Novel Clustering Protocol in Cognitive Radio Sensor Networks for Internet of Things

Meng Zheng et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

Proceedings Paper Automation & Control Systems

Distributed Model Predictive Load Frequency Control of multi-area Power Grid: A Decoupling Approach

Eleftherios E. Vlahakis et al.

IFAC PAPERSONLINE (2019)

Article Automation & Control Systems

A power system nonlinear adaptive decentralized controller design

Rui Yan et al.

AUTOMATICA (2010)

Article Automation & Control Systems

Consensus problems in networks of agents with switching topology and time-delays

R Olfati-Saber et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2004)