Multi-agent System Approach Research Articles

A multi-agent system approach for real-time energy management and control in hybrid low-voltage microgrids

The real-time operation of microgrids is crucial due to its ability to enhance energy resilience and efficiency. Continuous monitoring and adaptation to fluctuations in energy supply and demand ensure a stable and reliable electricity supply to local communities, even in the case of grid disturbances. This article presents an efficient and easily implementable real-time energy management and control system based on multi-agent systems for hybrid Low-Voltage Micro-Grids (LVMGs) using energy storage systems and renewable sources. The main objective of the proposed approach is to determine optimal setpoints for all microgrid components to improve overall efficiency and reduce electricity costs while satisfying multiple constraints. The well-defined flexibilities of all microgrid components to adjust their power are exploited to achieve optimal power allocation. The approach is tested and validated by developing a simulation environment using the Java Agent Development (JADE) framework, enabling the implementation of microgrid optimization through MATLAB/Simulink.

An Improved Predefined-Time Adaptive Neural Control Approach for Nonlinear Multiagent Systems

An Improved Predefined-Time Adaptive Neural Control Approach for Nonlinear Multiagent Systems

Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A Multi-agent System Approach in Karaj Plain, Iran

Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A Multi-agent System Approach in Karaj Plain, Iran

A novel development of advanced control approach for battery-fed electric vehicle systems

In today’s context, there is a clear preference for DC microgrids over AC microgrids due to their better compatibility with generating sources, loads, and battery energy storage systems (BESS). However, the intermittent nature of renewable resources disrupts the balance between power generation and load demand. It raises concerns regarding power management and quality in the power system. Control strategies are essential to address these challenges. This article focuses on developing a novel control strategy to ensure stability in microgrid systems. The proposed control structure utilizes a second-order multi-agent system (MAS) to enhance the power-sharing and coordination in the microgrid network. For effective control of battery energy storage units, a Voltage–Power (V-P) reference-based droop control and leader–follower consensus method is employed. The control approach consists of primary and secondary control layers. The primary layer uses a V-P reference-based droop control strategy to allocate load components to storage units. The secondary control layer aims to restore DC bus voltage using a MAS-based consensus protocol. The MAS approach offers greater flexibility and requires less computational power than other strategies such as Model Predictive Control (MPC). The enhanced control structure incorporates a current ratio modification loop to adjust the current ratio between the converters, thereby modifying gain and improving the voltage profile. This novel control optimizes the reliability and stability of the proposed DC microgrid system. The effectiveness of the enhanced consensus-based secondary control strategy is demonstrated using the MATLAB/Simulink platform.

A Two-Stage Hybrid Stochastic–Robust Coordination of Combined Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network.

Decentralized Smart Energy Management in Hybrid Microgrids: An Evaluation of Operational Modes, Resource Optimization, and Environmental Impacts in Nigeria

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids

On implementing autonomous supply chains: A multi-agent system approach

Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible supply chains. To address these challenges, the concept of the autonomous supply chain (ASC), characterised by predictive and self-decision-making capabilities, has recently emerged as a promising solution. However, research on ASCs is relatively limited, with no existing studies specifically focusing on their implementations. This paper aims to address this gap by presenting an implementation of ASC using a multi-agent approach. It presents a methodology for the analysis and design of such an agent-based ASC system (A2SC). This paper provides a concrete case study, the autonomous meat supply chain, which showcases the practical implementation of the A2SC system using the proposed methodology. Additionally, a system architecture and a toolkit for developing such A2SC systems are presented. Despite limitations, this work demonstrates a promising approach for implementing an effective ASC system.

Enhancing stability and robustness in online machine shop scheduling: A multi-agent system and negotiation-based approach for handling machine downtime in industry 4.0

Autonomous factories require high levels of adaptability, flexibility, and resilience to react to uncertainties on the shop floor, such as machine downtime. This paper proposes a negotiation-based, partial rescheduling method, combined with an existing multi-agent system, to swap jobs between machines. The negotiations are restricted to machines within the same work center, giving rise to a partial reschedule. A learning algorithm is also utilized, allowing machines to individually learn how to evaluate proposed bids from other machines and adapt the bids to their current environment. The main objective is to minimize the mean weighted tardiness of all jobs. Computational results indicate an improvement of 10–30 tardiness, compared to continuous rescheduling and complete rescheduling methods. In addition, a decrease of 70–80 sensitivity analysis and analysis of the partial reschedule.

Adversarial Decision Making Against Intelligent Targets in Cooperative Multiagent Systems

In this paper we propose a distributed adversarial decision making approach for multi-agent system (MAS), which is supposed to detect a team of intelligent targets. The MAS is demanded to achieve the best detection benefit against the intelligent targets that can shelter part of their features and prevent the detection according to an expert defense policy. One of the key challenges is how to achieve higher detection benefit that is both determined by the target assignment action of the MAS and the non-predictable defense policy of the targets. To handle this problem, we first formulate the multi-agent decision making problem as a max-min problem of detection benefit and break it into separate parts that are easier to be optimized. Then we introduce a new variant of distributed alternating direction method of multipliers (ADMM) to search the optimal solutions under the worst defense policy that the targets choose. To overcome the lack of access to global convergence of multi-block ADMM, we add local additional variables to formulate a penalty for non-convex parts of the local objective function. The convergence to an equilibrium and the optimality of the detection benefit are empirically validated by numerical simulations. The influence of the parameter setting is also presented and can be regarded as a prior suggestion for real applications.

Discrete-Time Adaptive Control for Uncertain Scalar Multiagent Systems with Coupled Dynamics: A Lyapunov-Based Approach

Discrete-time architectures offer a distinct advantage over their continuous counterparts, as they can be seamlessly implemented on embedded hardware without the necessity for discretization processes. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based frameworks, are highly complex. As a result, various existing continuous-time results using adaptive control methods to deal with system uncertainties and coupled dynamics in agents of a multiagent system cannot be directly applied to the discrete-time context. Furthermore, compared to their continuous-time equivalent, discrete-time information exchange based on periodic time intervals is more practical in the control of multiagent systems. Motivated by these standpoints, in this paper, we first introduce a discrete-time adaptive control architecture designed for uncertain scalar multiagent systems without coupled dynamics as a preliminary result. We then introduce another discrete-time adaptive control approach for uncertain multiagent systems in the presence of coupled dynamics. Our approach incorporates observer dynamics to manage unmeasurable coupled dynamics, along with a user-assigned Laplacian matrix to induce cooperative behaviors among multiple agents. Our solution includes Lyapunov analysis with logarithmic and quadratic Lyapunov functions for guaranteeing asymptotic stability with both controllers. To demonstrate the effectiveness of the proposed control architectures, we provide an illustrative example. The illustrative numerical example shows that the standard discrete-time adaptive control in the absence of observer dynamics cannot guarantee the reference state vector tracking, while the proposed discrete-time adaptive control can ensure the tracking objective.

Distributed Fault Tolerant Consensus Control of Nonlinear Multiagent Systems via Adaptive Dynamic Programming.

This article develops a distributed fault-tolerant consensus control (DFTCC) approach for multiagent systems by using adaptive dynamic programming. By establishing a local fault observer, the potential actuator faults of each agent are estimated. Subsequently, the DFTCC problem is transformed into an optimal consensus control problem by designing a novel local value function for each agent which contains the estimated fault, the consensus errors, and the control laws of the local agent and its neighbors. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each agent, a critic-only structure is established to obtain the approximate local optimal consensus control law of each agent. Moreover, by using Lyapunov's direct method, it is proven that the approximate local optimal consensus control law guarantees the uniform ultimate boundedness of the consensus error of all agents, which means that all following agents with potential actuator faults synchronize to the leader. Finally, two simulation examples are provided to validate the effectiveness of the present DFTCC scheme.

Hierarchical Coordination Control for Interconnected Operation of Electric-thermal-gas Integrated Energy System with Micro-energy Internet Clusters

At present, the natural gas network, thermal network and power grid can be coupled in the integrated energy system(IES) to provide users with different forms of energy such as heat, electricity and gas, but the relevant coordinated control strategies have not been sufficiently investigated. In this paper, a hierarchical distributed coordinated control strategy is proposed. The system is organized into three levels, namely IES-level, Micro-energy Internet(MEI)-level and component-level based on multi-agent system approach. To achieve accurate control of voltage and frequency of distributed devices and accurate distribution of the tie-line power in case of load fluctuations in MEI, a second-level optimization controller based on distributed sparse communication network is designed. In addition, in order to analyze the impact of communication delay on the coordinated control of MEI-level agents, the communication topology is optimized according to the basic graph theory. Finally, taking IES with three MEIs as an example, the validity of the proposed control strategy is verified on the PSCAD/EMTDC simulation platform.

A Multi-Agent System Approach for Balance Disorder Treatment: Integrating Computer Vision and Gamification

A Multi-Agent System Approach for Balance Disorder Treatment: Integrating Computer Vision and Gamification

Multi-Agent Systems and Foundation Models Enable Autonomous Supply Chains: Opportunities and Challenges

The demand for resilient and flexible supply chains has become even more apparent in the face of disruptions such as the COVID-pandemic and the ongoing Russia-Ukraine War. Whilst multi-agent system approaches have been proposed to increase resilience in supply chains for more than two decades, their development remains limited due to their difficulty of implementation and black box nature. The emergence of modern AI approaches including foundation models, enables the creation of generalist agents with multi-faceted decision-making capabilities. This opens up opportunities to create supply chain systems with self-orchestrating capabilities and heightened resilience, in a way that is human understandable, through natural language text. However, unlike areas such as healthcare and finance, the application of modern agent technology in the supply chain domain is under explored. This paper thus aims to conceptually explore this less studied domain, investigating the convergence between foundation models, multi-agent systems, and supply chain management. We discuss the opportunities and challenges arising from this convergence for enabling autonomous supply chains, and propose key future research topics to advance this convergence.

Exploring the Use of Multiagent Systems in Educational Robotics Activities

Multi-Agent Systems (MAS) have been demonstrated to be a useful tool for addressing complex problems by breaking them down into simpler tasks. In this paper, we explore the potential of the MAS approach in designing educational robotics learning experiences. Specifically, we aim to identify the pedagogical possibilities and technical considerations of using MAS in designing robot-based activities. We have designed three educational robotics experiences based on the MAS approach. The first experience describes the use of the MAS approach in creating a learning activity based on Multi-Robots Systems (MRS). The second experience describes the use of the MAS approach for implementing theater-based activities with MRS. Finally, the third experience outlines the implementation of an artificial cognitive architecture based on MAS to control the educational actions of an anthropomorphic social robot. The results reveal that MAS and MRS offer opportunities to design distributed learning environments with distributed artificial intelligence.

Auction-based single buyer energy trading framework in grid-tied microgrid with distributed energy storage and demand response using a multi-agent approach

This paper presents a modified single-buyer energy trading model for a grid-tied microgrid that uses a multi-agent system (MAS) approach in a two-stage market auction for multiple sellers and multiple buyers (MSMB).Additionally, a new linear bidding algorithm (LBA) is made available for use by stakeholders (sellers and buyers) in order to select the quote prices for day-ahead trade. By utilising available distributed generation (DG), distributed storage systems (DSS), and demand response (DR) in the first stage (local) market, if not forwarded to the second stage (global) market, this strategy seeks to reduce peak demand and maximise cost benefits for stakeholders and aggregators. The suggested market framework is simulated using MATLAB and Simulink. The simulation results of a test system, which indicate the differences before and after DR and DSS (DRaDS) are taken into consideration, are used to emphasise the effects of DRaDS on market clearing prices from the proposed two-stage market auction.

Extended review of multi-agent solutions to Advanced Public Transportation Systems challenges

AbstractOver the past few decades, intelligent transportation systems (ITS) have emerged as an effective way to improve the performance of transportation systems. ITS provide innovative services, enhance travel safety, provide travellers with more choices, and make transportation systems more efficient. Multi-agent systems (MAS), which define autonomous interacting entities, are suitable for modelling distributed and intelligent systems in general and ITS in particular. This paper provides an in-depth review of multi-agent systems applied to Advanced Public Transportation Systems (APTS), a subclass of ITS dedicated to managing public transportation networks. We carefully analysed 38 papers in this study, published in 19 journals during 31 years (1990–2020). We perform a synthetic analysis of the trends in this domain and a qualitative analysis focused on multi-agent systems’ dimensions and properties. We show that the MAS approach is well suited to the real-time management of disturbances thanks to their delegation process, and their pro-activeness and autonomy properties.

A normative approach for resilient multiagent systems

A normative approach for resilient multiagent systems

A Multi-Agent Systems Approach for Optimized Biomedical Literature Search

A Multi-Agent Systems Approach for Optimized Biomedical Literature Search

A Genetic Programming-based Framework for Semi-automated Multi-agent Systems Engineering

With the rise of new technologies, such as Edge computing, Internet of Things, Smart Cities, and Smart Grids, there is a growing need for multi-agent systems (MAS) approaches. Designing multi-agent systems is challenging, and doing this in an automated way is even more so. To address this, we propose a new framework, Evolved Gossip Contracts (EGC). It builds on Gossip Contracts (GC), a decentralised cooperation protocol that is used as the communication mechanism to facilitate self-organisation in a cooperative MAS. GC has several methods that are implemented uniquely, depending on the goal the MAS aims to achieve. The EGC framework uses evolutionary computing to search for the best implementation of these methods. To evaluate EGC, it was used to solve a classical NP-hard optimisation problem, the Bin Packing Problem (BPP). The experimental results show that EGC successfully discovered a decentralised strategy to solve the BPP, which is better than two classical heuristics on test cases similar to those on which it was trained; the improvement is statistically significant. EGC is the first framework that leverages evolutionary computing to semi-automate the discovery of a communication protocol for a MAS that has been shown to be effective at solving an NP-hard problem.