MPC-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR

Dutta, Arunima; Ganguly, Sanjib; Kumar, Chandan

doi:10.1109/tia.2022.3163108

Cited by 26 publications

(11 citation statements)

References 25 publications

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“…2) was estimated at around 5-20 µs which is well within the observer sampling time of 100 µs. 6 Thus, even the worstcase sum of computation times and communication delays is less than the allotted delay interval of 100 ms (N d = 1 and T s = 0.1 s). Additionally, there were zero overruns detected by the OPAL-RT, which indicates that the simulation was successfully performed in real-time.…”

Section: B Timing Analysis Of Ltmpc and Ekfmentioning

confidence: 98%

See 1 more Smart Citation

Linearized-Trajectory Model-Predictive Controller for Improving Microgrid Short-Term Stability With Real-Time Implementation

Hamilton

Navarro

Aliprantis

et al. 2023

IEEE Trans. on Ind. Applicat.

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A model-predictive controller (MPC) for synchronous generator-based microgrids is introduced to enhance voltage, frequency, and transient stability in fast timescales. The centralized controller uses mathematical models of power system dynamics to predict the evolution of system states over a finite horizon based on information from local state observers and relays. The MPC dynamically adjusts the setpoints of existing primary controls to maximize system stability. To address the computational challenges of a real-time MPC implementation due to nonlinear power system dynamics and short sampling intervals, a trajectory linearization technique is applied to the MPC formulation. The proposed controller is validated via a hardware-in-the-loop (HIL) testbed. The testbed includes an implementation of the controller in hardware, which interfaces with a high-fidelity microgrid simulation model running in a realtime simulator. State observers based on the Extended Kalman Filter (EKF), which provide the controller with estimates of the system state, are also implemented. The HIL testbed is used to verify the real-time operation of the controller and evaluate its performance under modeling uncertainties.

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Section: B Timing Analysis Of Ltmpc and Ekfmentioning

confidence: 98%

“…A brief review of recent work on MPC-based power system control is provided next. MPC-based secondary control structures to ensure long-term voltage and frequency stability have been proposed [2]- [6]. These instability mechanisms develop over timescales of several minutes, and are primarily due to power flow imbalance in a network.…”

Section: Introductionmentioning

confidence: 99%

Linearized-Trajectory Model-Predictive Controller for Improving Microgrid Short-Term Stability With Real-Time Implementation

Hamilton

Navarro

Aliprantis

et al. 2023

IEEE Trans. on Ind. Applicat.

View full text Add to dashboard Cite

show abstract

“…Proposed model integrates multiple control devices, including Maximum Power Point Tracking (MPPT), Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Fuzzy Logic Controllers (FLC) [4], which are selectively controlled by a bio-inspired optimization technique to determine the optimal parameters and configurations [5]. Furthermore, Model incorporates a predictive control operation using a VARMA-GRU (Vector Auto-regressive Moving Average-Gated Recurrent Unit) with an LSTM (Long Short-Term Memory) model to accurately predict future power generation, which can facilitate more efficient energy management capabilities [6].…”

Section: Introductionmentioning

confidence: 99%

EPCMSDB: Design of an ensemble predictive control model for solar PV MPPT deployments via dual bioinspired optimizations

Hole,

Das Goswami

2024

Sci. Tech. Energ. Transition

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With the increasing demand for renewable energy, solar power has emerged as a promising option for sustainable power generation. However, the effectiveness and efficiency of solar power systems rely on the ability to effectively manage their performance, making it essential to develop efficient control models. This paper proposes a novel ensemble predictive control model for solar deployments using bio-inspired optimizations to improve load-connected solar deployments’ performance. The proposed model integrates multiple control devices, including Maximum Power Point Tracker, Proportional-Integral-Derivative, Proportional-Integral, and Fuzzy Logic Controllers, to selectively control the solar Photovoltaic systems. The proposed model incorporates a predictive control operation utilizing an LSTM-GRU (Long Short-Term Memory-Gated Recurrent Unit) with the VARMA (Vector Auto-Regressive Moving Average) model, which can accurately predict the future power generation of the solar system. This feature can facilitate efficient energy management and increase the system’s performance for different use cases. Implement a SEPIC (Single Ended Primary Inductor Capacitor) converter design to improve the system’s overall efficiency levels. To validate the effectiveness of the proposed approach, the author conducted experiments using real-world data and compared the proposed results with other control strategies. The results demonstrate that the ensemble predictive control model based on bio-inspired optimizations outperforms the existing control models regarding accuracy, efficiency, and stability levels. The proposed model has the potential to significantly improve the performance of load-connected solar deployments, offering a more practical approach to solar power generation. The combination of predictive control operations with bio-inspired optimizations can facilitate the design of sustainable energy systems with higher efficiency and accuracy.

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“…In refs. [24][25][26][27], centralized voltage control schemes are adopted to maintain voltage stability in an active distribution grid context. The voltage regulation levels are voltage source converters (VSCs), considered reactive power sources, on-load tap changers (OLTC), and step voltage regulators (SVRs).…”

Section: Introductionmentioning

confidence: 99%

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

Bragatto

Bucarelli

et al. 2023

Sensors

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The increasing penetration of distributed generation (DG) across power distribution networks (DNs) is forcing distribution system operators (DSOs) to improve the voltage regulation capabilities of the system. The increase in power flows due to the installation of renewable plants in unexpected zones of the distribution grid can affect the voltage profile, even causing interruptions at the secondary substations (SSs) with the voltage limit violation. At the same time, widespread cyberattacks across critical infrastructure raise new challenges in security and reliability for DSOs. This paper analyzes the impact of false data injection related to residential and non-residential customers on a centralized voltage regulation system, in which the DG is required to adapt the reactive power exchange with the grid according to the voltage profile. The centralized system estimates the distribution grid state according to the field data and provides the DG plants with a reactive power request to avoid voltage violations. A preliminary false data analysis in the context of the energy sector is carried out to build up a false data generator algorithm. Afterward, a configurable false data generator is developed and exploited. The false data injection is tested in the IEEE 118-bus system with an increasing DG penetration. The false data injection impact analysis highlights the need to increase the security framework of DSOs to avoid facing a relevant number of electricity interruptions.

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MPC-Based Coordinated Voltage Control in Active Distribution Networks Incorporating CVR and DR

Cited by 26 publications

References 25 publications

Linearized-Trajectory Model-Predictive Controller for Improving Microgrid Short-Term Stability With Real-Time Implementation

Linearized-Trajectory Model-Predictive Controller for Improving Microgrid Short-Term Stability With Real-Time Implementation

EPCMSDB: Design of an ensemble predictive control model for solar PV MPPT deployments via dual bioinspired optimizations

False Data Injection Impact on High RES Power Systems with Centralized Voltage Regulation Architecture

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