Autonomous Systems and Intelligent Agents in Power System Control and Operation

Rehtanz, Christian

doi:10.1007/978-3-662-05955-5

Cited by 135 publications

(66 citation statements)

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“…The particles in the swarm were defined as described above by the 27-dimensional vector, x. The basic form of the fitness function of the PSO was similar to that in Equation (19), except that it was modified to the form of the average of cn cases. The optimized FIS was determined from the final output of the PSO algorithm, which was oriented only to that target distribution system.…”

Section: Optimization Of the Fuzzy Inference Systemmentioning

confidence: 99%

“…The objectives of our problem are to minimize the reactive power cost function defined as Equation (18) while maintaining the last bus voltage at the predetermined reference, which is 0.95 in this case. The optimization problem has a similar form as Equation (19), and it can be represented as:…”

Section: Conflicts Of Interestmentioning

confidence: 99%

“…A common concept of these works has been using the compensation ability of DGs and operating multiple DGs in an autonomous and distributed manner. The application of a multi-agent system (MAS) to power engineering has emerged over the past decade [18,19]. MAS is a system comprising two or more agents that have decision-making capabilities and can communicate with other agents.…”

Section: Introductionmentioning

confidence: 99%

“…There have been a number of papers of voltage control based on MAS [19,[21][22][23]. The work in [19] describes a theoretical multi-agent secondary voltage control scheme. Voltage compensation using the reactive power output of DGs based on MAS has been proposed [21].…”

Section: Introductionmentioning

confidence: 99%

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Voltage Control Method Using Distributed Generators Based on a Multi-Agent System

2015

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This paper presents a voltage control method using multiple distributed generators (DGs) based on a multi-agent system framework. The output controller of each DG is represented as a DG agent, and each voltage-monitoring device is represented as a monitoring agent. These agents cooperate to accomplish voltage regulation through a coordinating agent or moderator. The moderator uses the reactive power sensitivities and margins to determine the voltage control contributions of each DG. A fuzzy inference system (FIS) is employed by the moderator to manage the decision-making process. An FIS scheme is developed and optimized to enhance the efficiency of the proposed voltage control process using particle swarm optimization. A simple distribution system with four voltage-controllable DGs is modeled, and an FIS moderator is implemented to control the system. Simulated data show that the proposed voltage control process is able to maintain the system within the operating voltage limits. Furthermore, the results were similar to those obtained using optimal power flow calculations, even though little information on the power system was required and no power flow calculations were implemented.

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Section: Optimization Of the Fuzzy Inference Systemmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Voltage Control Method Using Distributed Generators Based on a Multi-Agent System

2015

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“…In recent years, several researchers have proposed multiagent designs as a way to increase performance of these systems [11], [12]. While all of these have essentially been academic exercises, their introduction has merit in the fact that they consider multi-level autonomy and peer negotiation for resources.…”

Section: A Power Transmission Substationmentioning

confidence: 99%

Notional examples and benchmark aspects of a resilient control system

Rieger

2010

2010 3rd International Symposium on Resilient Control Systems

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-Digital control system technology has pervaded most industries, leading to improvements in the efficiency and reliability of the associated operations. However, the ease of distributing and connecting related control systems for the purposes of increasing performance has resulted in interdependencies that can lead to unexpected conditions. Even with less complex designs, operators and engineers alike are often left with competing goals that are difficult to resolve. A fundamental reason for this dichotomy is that responsibilities lie with different disciplines, and operations are hosted on separate control systems. In addition, with the rising awareness of cyber security and diverse human interactions with control systems, an understanding of human actions from a malicious and benevolent standpoint is necessary. Resilience considers the multiple facets of requirements that drive the performance of control systems in a holistic fashion, whether they are security or stability, stability or efficiency, human interactions or complex interdependencies. As will be shown by example, current research philosophies lack the depth or the focus on the control system application to satisfy these requirements, such as graceful degradation of hierarchical control while under cyber attack. A resilient control system promises to purposefully consider these diverse requirements, developing an adaptive capacity to complex events that can lead to failure of traditional control system designs.

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Python‐based multi‐agent platform for application on power grids

Melo

Sampaio

Leão

et al. 2019

Int Trans Electr Energ Syst

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Summary Concerns about the future energy needs challenge energy experts to present advanced power grids making them smarter. Smart grids employ digital technology and are based on highly collaborative and responsive decision‐making strategies. Current trends to control and manage their operation lead to the use of multi‐agent system (MAS) technology. This paper aims to present the design, specification, and application of the Python Agent DEvelopment (PADE) framework, an open source platform implemented in Python language and conceived for the implementation of multi‐agent systems on power systems. PADE is compliant with specifications of the Foundation for Intelligent Physical Agents and eases the development of solutions to power systems based on MAS. For demonstration of the effectiveness of PADE, an automatic restoration system MAS‐based is presented. A test bed that encompasses agents embedded on computation devices is used to demonstrate the practical implementation of a PADE‐based multi‐agent systems in a power distribution network.

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Autonomous Systems and Intelligent Agents in Power System Control and Operation

Cited by 135 publications

References 0 publications

Voltage Control Method Using Distributed Generators Based on a Multi-Agent System

Voltage Control Method Using Distributed Generators Based on a Multi-Agent System

Notional examples and benchmark aspects of a resilient control system

Python‐based multi‐agent platform for application on power grids

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