A Heterogeneous Multi-Agent System Model With Navigational Feedback for Load Demand Management of a Zonal Medium Voltage DC Shipboard Power System

Du, Wenzeng; Yang, Genke; Pan, Changchun; Xi, Peifeng

doi:10.1109/access.2019.2946644

Cited by 12 publications

(7 citation statements)

References 24 publications

(42 reference statements)

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“…Maximizes performance and sustain change for a long period of time tal characteristics, which are reactivity, pro activeness, and social ability" [85]. Because of the decentralized nature of the demand-side in power systems, there is a need for approaches that can learn, plan, and make decisions in a complex environment involving a large number of interconnected intelligent agents [86]. One of the most promising areas of research right now is multi-agent systems (MAS), a sub-area of distributed artificial intelligence that provides the capability to examine these challenges.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

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Section: Reinforcement Learningmentioning

confidence: 99%

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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“…Besides the power source management, demand response can also be implemented to achieve power balance. Different from load management for terrestrial power systems, of which the managing objects more focus on economical operation, the load management of SPSs is responsible for enhancing system stability by avoiding the sources being overloaded [59]. To achieve this object, the shipboard loads are classified into three types depending on their priority: particularly vital, vital, and non-vital loads [60].…”

Section: E Energy Management In Dc-smgsmentioning

confidence: 99%

A Review of DC Shipboard Microgrids—Part II: Control Architectures, Stability Analysis, and Protection Schemes

Guerrero

Lashab

et al. 2022

IEEE Trans. Power Electron.

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This paper presents a review of coordinated control strategies, stability analysis, and fault management for DC shipboard microgrids (DC-SMGs). As an emerging application, the DC-SMG lacks a comprehensive summary of the control schemes. Considering the specific load profile in ships, this paper discusses the coordinated control strategies of diesel generators and energy storage system (ESS). Depending on the maritime conditions, different operation modes are switched by the energy management system (EMS). Due to the presence of highbandwidth controlled constant power load (CPL) converters, negative impedance instability is induced. Meanwhile, the pulsed loads in DC-SMG may affect the voltage stability significantly. Small-signal and large-signal stability analysis can be used to evaluate the effect of these two types of loads. In the aspect of shipboard protection system, this paper presents the specific requirements of DC-SMG based on the standards. Lacking of 'ground' in ships, solutions in designing the grounding system for DC-SMG is described. Besides, the short-circuit fault management consisting of fault detection, fault isolation, and reconfiguration in DC-SMGs are summarized. Finally, existing commercial DC ships are presented and compared to demonstrate the developing trends.

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“…As a sub-field of AI, MAS has excellent portability. Therefore, MAS has been widely adopted to model systems with multi-participators, which are widespread in numerous fields including communication protocol [49], cooperative control [50]- [51], fault-tolerant control [52], electrical power system [53], sensor deployment [54], transportation [55]- [57], etc. MAS researchers focus on structural mechanisms of complex systems.…”

Section: B Multi-agent Reinforcement Learning (Marl)mentioning

confidence: 99%

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

Pan

Chen

et al. 2020

IEEE Access

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The Dynamic Traffic Assignment (DTA) is one of the important measures to alleviate urban network traffic congestion. The congestions are usually caused by stochastic traffic demands, which are generally unassignable from time dimension in the real-world but are assumed to be assignable in existing DTA methods (i.e. real-time travel demands). In this paper, a distributed DTA method for preventing urban network traffic congestion caused by stochastic real-time travel demands by improving Multi-Agent Reinforcement Learning (MARL). A team structure, which consists of decision-makers and advisers, is designed to learn parallelly in realistic DTA tasks. To reduce the size of the solution space adaptively, the dynamic critical values advised by adviser agents are adopted as constraints for the strategy space of decision-makers (i.e. main agents). A collaborative heterogeneous-adviser mechanism is designed to avoid deviation of guidance. To enhance the adaptability of DTA to the changeable external environment, the mixed strategy concept is introduced to improve the decision-making process of main agents. The respective mapping mechanisms are designed to define adaptive learning rates to improve the sensitivity of MARL. The Sioux Falls (SF) network is established as a test platform via a Dynamic Network Loading (DNL). The effectiveness of the suggested DTA method is assessed through numerical simulations SF network. Under the influence of the scenario with stochastic real-time travel demands, the results show that the proposed method outperforms in terms of the throughput of the network and the individual average travel time among the overall network. Additionally, the ability of the proposed method in response to the external environment rapidly has also been demonstrated. Adopting the suggested method can improve the state of the art to assign stochastic real-time travel demands dynamically and to avoid potential traffic congestion fundamentally. INDEX TERMS dynamic traffic assignment, intelligent transportation system, multi-agent system, reinforcement learning, multi-agent reinforcement learning, numerical simulation.

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A Heterogeneous Multi-Agent System Model With Navigational Feedback for Load Demand Management of a Zonal Medium Voltage DC Shipboard Power System

Cited by 12 publications

References 24 publications

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

A Review of DC Shipboard Microgrids—Part II: Control Architectures, Stability Analysis, and Protection Schemes

A Distributed Assignment Method for Dynamic Traffic Assignment Using Heterogeneous-Adviser Based Multi-Agent Reinforcement Learning

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