Multi-agent based distributed voltage regulation scheme with grid-tied inverters in active distribution networks

Arshad, Ammar; Ekström, Jussi; Lehtonen, Matti

doi:10.1016/j.epsr.2018.02.015

Cited by 11 publications

(5 citation statements)

References 21 publications

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“…where S IP and S IQ are the sensitivity matrices with respect to the real and respective part of current, whereas R and X are the real and reactive part of impedances in the impedance matrix [Z] [30]. Fig.…”

Section: A = [Donothing Valleyf Illing Shif Ting]mentioning

confidence: 99%

Combined DR Pricing and Voltage Control Using Reinforcement Learning Based Multi-Agents and Load Forecasting

et al. 2022

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The demand for energy around the world continues to increase at a very high rate. To sufficiently supply this high demand, it is imperative to employ efficient methods so that the total costs for fulfilling such high demand in energy are minimized. To achieve this ambitious goal, this paper proposes a multi-agent reinforcement learning system for time of use pricing based combined demand response and voltage control. For this purpose, a long short term memory network is employed for day-ahead load forecasting in order to remove future uncertainties. The Q-learning algorithm is used which is a model free algorithm and hence, doesn't require the agent(s) to have prior knowledge of the environment. The role of reinforcement learning in this work is very important since it allows the agent(s) to determine their respective optimal behavior(s) autonomously without explicit training by the end user. To allow effective cooperation among multiple agents, each household is controlled by its own agent, whereas all the household agents are directed by a master agent or service provider. Accordingly, the voltage control agent serves the purpose of checking voltage level violations in the system and removing them through optimal decision making. The proposed system yields very good results, whereby, not only is the overall cost of electricity reduced, but voltage level violations are also removed from the entire system. The implementation of this mechanism reduces the total average aggregated load demand from 5.23 kW to 3.86 kW, while reducing the total aggregated average cost from 94.01 Rs to 60.80 Rs, thanks to the proposed effective multi-agent based system.INDEX TERMS Reinforcement learning, long short term memory, demand response, multi-agent system, voltage control

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Section: A = [Donothing Valleyf Illing Shif Ting]mentioning

confidence: 99%

Combined DR Pricing and Voltage Control Using Reinforcement Learning Based Multi-Agents and Load Forecasting

et al. 2022

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“…The distributed approach of routing regional market demand examined in [25] and trendy applications of multiagent in [26] are analyzed. The power network control optimization, islanding LV network, and voltage regulation, by implementing multiagent systems [27]- [30].…”

Section: Related Workmentioning

confidence: 99%

“…Whereas, optimized power management is more efficient than centralized control systems. The consensusability inspired in control [17], [36] and autonomic computing [21], [30], [32], [35] applications are mostly implemented in this paper. The network consists of several distributed nodes reaching an agreement of mutual interest of initial state variable.…”

Section: Problem Formulationmentioning

confidence: 99%

Power Mismatch Estimation in Smart Grid Using Distributed Control

2020

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One of the major challenges in the area of smart grids is the management of power between consumers and generators. Traditionally, the power mismatch is managed in a centralized fashion which has major shortcomings of complexity, requires large bandwidth, ineffectiveness, and unscalable. To address these problems, this paper presents a novel distributed mismatch technique in smart grids. In this algorithm, every generation and consumer unit, has to estimate the total power that has been generated, the total load and the power mismatches. The coordination and control of power nodes is achieved through distributed manner. The proposed technique achieves through consensus algorithms. Such distributed technique prevails task sharing, surviving on single link failure, efficient decision making, the fastest convergence, and autonomy for the global power nodes. The technique is suitable for all types of grid in islanded and connected mode. We evaluated optimization factors: rapid convergence, fast computation, scalability and effectiveness. The proposed distributed network examined power systems using random, unreliable, unpredicted, and arbitrary topologies. It explores distributed node convergence, optimality, and status sharing through Graphs and Matrix theories. The communication reliability, link stability, privileges distribution, comparative cost, and adoptability of propose distributed technique has been assessed. Moreover, the proposed scheme is evaluated under different communication topologies and experimental testbed results to explain the effectiveness of the algorithm.

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“…Multiple agents can collect and process data in parallel, respond to changes of environment swiftly and cooperate with each other in a highly efficient way. Various applications of MAS in the distribution network have been explored, covering protection and control [2], [3], voltage regulation [4] and load management [5]- [7]. The state-of-art achievements on MAS-based self-healing techniques are summarized in [8].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent-Based Self-Healing Framework Considering Fault Tolerance and Automatic Restoration for Distribution Networks

2021

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Increasing penetration of distributed generators (DGs) brings new challenges to the control and protection of distribution networks. This article proposes a control and protection framework based on Multi-agent System (MAS), in which situation awareness of zone agents plays an important role. The main protection is designed to locate and isolate faults based on the peer-to-peer communication among the zone agents. Fault-tolerant mechanisms, including self-inspection and backup protection, are developed to cope with the malfunction of individual MAS and local communication failure. An automatic parallel restoration scheme that doesn't require complete grid information is proposed to restore the power supply. Comprehensive simulation studies are carried out on a 71-bus distribution network with DGs using DIgSILENT, and the results have shown the effectiveness of the proposed self-healing framework.

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Multi-agent based distributed voltage regulation scheme with grid-tied inverters in active distribution networks

Cited by 11 publications

References 21 publications

Combined DR Pricing and Voltage Control Using Reinforcement Learning Based Multi-Agents and Load Forecasting

Combined DR Pricing and Voltage Control Using Reinforcement Learning Based Multi-Agents and Load Forecasting

Power Mismatch Estimation in Smart Grid Using Distributed Control

A Multi-Agent-Based Self-Healing Framework Considering Fault Tolerance and Automatic Restoration for Distribution Networks

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