Multi-agent-Based Decision Support Systems in Smart Microgrids

Salgueiro, Yamisleydi; Rivera, Marco; Nápoles, Gonzalo

doi:10.1007/978-981-13-8311-3_11

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…A detailed survey about multi-agent systems was analyzed in [10] that focused on distributed intelligence of agents that can communicate with each other by participating and managing their demand and load to achieve their target. Besides, a study of a multiagent based decision support system based on recent applications to the related fields was presented in [11], where they discussed how intelligence can be applied through sensing environment in making decisions from a different point of views. A multi-agent based decentralized autonomous smart grid with communication constraints was investigated in [12], where the agent can establish communication with neighboring agents considering system frequency.…”

Section: B Literature Reviewmentioning

confidence: 99%

Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks

Imteaj¹,

Amini²,

Mohammadi³

2019

Preprint

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This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires. A recent example is PG&E's forced power outage to contain wildfire risk which led to widespread power disruption. This paper focuses on answering two questions: who is responsible for resilience? and how to quantify the monetary value of resilience? To this end, we first provide preliminary definitions of resilience for power systems. We then investigate the role of natural hazards, especially wildfire, on power system resilience. Finally, we will propose a decentralized strategy for a resilient management system using distributed storage and demand response resources. Our proposed high fidelity model provides utilities, operators, and policymakers with a clearer picture for strategic decision making and preventive decisions.

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Section: B Literature Reviewmentioning

confidence: 99%

Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks

Imteaj¹,

Amini²,

Mohammadi³

2019

Preprint

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“…Although the fuzzy-based RBS and neural network schemes are more adaptable, still, they can produce unreliable results [32]. On the other hand, to ensure the robustness, reliability, and flexibility of MGs, MAS based decentralized control approaches have been recommended for MG management [25,30,37,38]. The prominent MAS intelligent agent's characteristics in the different aspects are classified as [32]: (i) reactivity: the ability to react to changes in the environment in a timely manner; (ii) pro-activeness: goal-directed behavior; (iii) social-ability: interaction with other agents.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids

2020

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The main purpose of this paper is to present a novel algorithmic reinforcement learning (RL) method for damping the voltage and frequency oscillations in a micro-grid (MG) with penetration of wind turbine generators (WTG). First, the continuous-time environment of the system is discretized to a definite number of states to form the Markov decision process (MDP). To solve the modeled discrete RL-based problem, Q-learning method, which is a model-free and simple iterative solution mechanism is used. Therefore, the presented control strategy is adaptive and it is suitable for the realistic power systems with high nonlinearities. The proposed adaptive RL controller has a supervisory nature that can improve the performance of any kind of controllers by adding an offset signal to the output control signal of them. Here, a part of Denmark distribution system is considered and the dynamic performance of the suggested control mechanism is evaluated and compared with fuzzy-proportional integral derivative (PID) and classical PID controllers. Simulations are carried out in two realistic and challenging scenarios considering system parameters changing. Results indicate that the proposed control strategy has an excellent dynamic response compared to fuzzy-PID and traditional PID controllers for damping the voltage and frequency oscillations.

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Energy Management System for Smart Microgrid Systems using Adaptive Fuzzy Logic

Patil,

Patil

2023

2023 3rd International Conference on Intelligent Technologies (CONIT)

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Multi-agent-Based Decision Support Systems in Smart Microgrids

Cited by 6 publications

References 29 publications

Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks

Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks

Assessing the Use of Reinforcement Learning for Integrated Voltage/Frequency Control in AC Microgrids

Energy Management System for Smart Microgrid Systems using Adaptive Fuzzy Logic

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