Energy management and harmonic compensation of micro‐grids via multi‐agent systems based on decentralized control architecture

Salehirad, Mohammadreza; Emamzadeh, Mohammad Mollaie

doi:10.1049/rpg2.12653

Cited by 10 publications

(7 citation statements)

References 43 publications

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“…Given today's power scenarios, centralized control is more difficult for a competitive open electricity market environment [12]. Moreover, it requires complex communication networks, which reduces the system's reliability [19]. Therefore, a communication-free decentralized control is more appropriate for a competitive electricity market with high reliability and speed of response.…”

Section: Literature Reviewmentioning

confidence: 99%

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Decentralized frequency control of restructured energy system using hybrid intelligent algorithm and non‐linear fractional order proportional integral derivative controller

Irudayaraj

Wahab

Veerasamy

et al. 2023

IET Renewable Power Gen

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The emergence of micro‐sources in the energy market to reduce carbon emissions and exploit more renewable generations increases the frequency oscillations of the system. Hence, this paper attempts to develop a robust nonlinear fractional order proportional integral derivative (NLFOPID) controller for frequency regulation of restructured energy systems. A hybrid atom search‐particle swarm optimization (AS‐PSO) is proposed to optimize the gain values of the NLFOPID controller. The proposed control approach enhances flexibility by providing robustness against the RE intermittency. The optimization technique is coded in MATLAB and applied for frequency regulation of a multi‐area energy system developed in Simulink. Initially, the effectiveness of the hybrid algorithm is validated for standard benchmark functions and then implemented for frequency control. The results demonstrate that the proffered AS‐PSO technique performs significantly for various types of transactions in a deregulated energy market than state‐of‐the‐art. The real‐time applicability of the proposed controller is validated using hardware‐in‐the‐loop simulation of the open energy market in OPAL‐RT. The frequency response obtained from the AS‐PSO optimized NLFOPID controller is remarkable compared to other techniques. Furthermore, the closed‐loop stability of the system is examined using bode analysis through an improved Oustaloup approximation technique.

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Section: Literature Reviewmentioning

confidence: 99%

“…The superiority of the PID controller is compared with various classical controllers such as proportional integral, integral, and integral derivative controllers. A four-area comprising thermal, gas, hydro, and diesel power plants is studied in [19]. Moreover, cascaded PID controllers are also implemented in deregulated environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Decentralized frequency control of restructured energy system using hybrid intelligent algorithm and non‐linear fractional order proportional integral derivative controller

Irudayaraj

Wahab

Veerasamy

et al. 2023

IET Renewable Power Gen

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“…In addressing the complex tasks and challenges of distributed energy generation and control, varieties of agent design models are explored. Accurate component modeling and coordination between agents are crucial for effective control [35]. Software agent has become a powerful and rapidly growing technology in power systems, enabling intelligent and autonomous agents to operate in microgrid environments.…”

Section: Spade Agentsmentioning

confidence: 99%

Smart Python Agents for Microgrids

Al-Agtash,

Xian,

Petroshchuk

et al. 2023

SGRE

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Microgrids are revolutionary power systems that interconnect a mix of renewable power generation, load, storage systems, and inverters in a smallscale grid network. Operating microgrids while maintaining a consistent grid voltage and frequency during the islanding and disruption of renewables has been a challenging research problem. In this paper, a preliminary microgrid agent implementation is presented using SPADE (Smart Python Agent Development Environment) as a powerful development framework that has been used extensively in many application domains. Agents autonomously managed and operated microgrid individual components. A multiagent microgrid system was modeled to seamlessly operate and optimize energy balance by coordinating the actions of agents. Agents were built to forecast energy demand and solar power and coordinate to balance generation with load while maintaining optimal power flow and adequate network voltage and frequency.

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“…Thus, an effective solution for managing an ecologically friendly renewable energy resource is highly required. Although various decentralized control and operation methods are currently under extensive research [3]- [4], system operators must heavily rely on centralized control, such as the energy management system (EMS). Owing to the supervisory control and data acquisition (SCADA) capability, it continuously monitors and regulates the operational status of generators, substations, and transmission lines, making it an integral component of our energy infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Steady-State Data-Driven Dynamic Stability Assessment in the Energy Management System

Song,

Min,

Jung

2024

Preprint

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The extensive research on dynamic security assessment stability prediction has focused on data preprocessing techniques to improve accuracy because it was assumed that high-resolution postfault data exist. For practical users, the acquisition and application of high-resolution measurement data present significant challenges. Installing phasor measurement units on all power system nodes is deemed impractical due to high costs. In this work, we aimed to develop a rotor angle stability prediction model using steady-state data that can be easily generated from current energy management system. Note that the steady-state measurement data refer to a pre-contingency operation condition characterized by real and reactive loads, generation levels, flows, as well as voltages and angles. The proposed framework comprises three stages: it finds physical meaning from the extended equal-area criterion to move away from the black-box approach, proposes a feature data extraction strategy to reduce the dimensionality of the input space in the support vector machine, and partition time-series power flow data by month to consider system topology changes. By utilizing 5-min-interval power flow data, unstable cases are determined, and two main feature data are extracted to train the support vector machine. The proposed model can be implemented in the energy management system for industrial use, and the obtained results showed the effectiveness of the proposed framework in responding to a critical line fault event in real time

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Energy management and harmonic compensation of micro‐grids via multi‐agent systems based on decentralized control architecture

Cited by 10 publications

References 43 publications

Decentralized frequency control of restructured energy system using hybrid intelligent algorithm and non‐linear fractional order proportional integral derivative controller

Decentralized frequency control of restructured energy system using hybrid intelligent algorithm and non‐linear fractional order proportional integral derivative controller

Smart Python Agents for Microgrids

Steady-State Data-Driven Dynamic Stability Assessment in the Energy Management System

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