Predictive Coordinated and Cooperative Voltage Control for Systems With High Penetration of PV

Mahdavi, Shahrzad; Panamtash, Hossein; Dimitrovski, Aleksandar; Zhou, Qun

doi:10.1109/tia.2021.3064910

Cited by 40 publications

(12 citation statements)

References 30 publications

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“…Voltage deviation is an important indicator of power quality. Excessive voltage deviation will do great harm to safety, stability, and economic operation [31]. The line loss minimization and voltage deviation minimization are regarded as a MOP.…”

Section: Objective Functionmentioning

confidence: 99%

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

2022

Sensors

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With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application.

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Section: Objective Functionmentioning

confidence: 99%

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

2022

Sensors

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“…Reactive power control is critical in the electrical grid to avoid voltage breakdown, voltage instability, and voltage rise when there is an unusual occurrence or eventualities at PCC. Voltage rise and instability occur in a system wherever there is insufficient of reactive power during RDG over generation, heavy loading and disturbances such as grid faults [29]. Installing Flexible Alternating Current Transmission System (FACTS) devices such as pulse width modulation distribution static compensator (PWMDSTATCOM) connected to the PCC of a large renewable farm, dynamic compensation of reactive power and voltage rise control capability can be realized since the lesser the voltage at PCC, the more the reactive power needed [30].…”

Section: Reactive Power Compensation Strategymentioning

confidence: 99%

Analysis of Voltage Rise Phenomena in Electrical Power Network With High Concentration of Renewable Distributed Generations

Akinyemi

Musasa

Davidson

2022

Preprint

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The increasing penetration levels of Renewable Distributed Generation (RDG) into power system have proven to bring both positive and negative impacts. The occurrence of under voltage at the far end of a conventional Distribution Network (DN) may not raise concern anymore with RDGs integration into the power system. However, a high penetration of RDG into power system may cause problems such as voltage rise or over-voltage and reverse power flows at the Point of Common Coupling (PCC) between RDG and DN. This research paper presents the voltage rise and reverse power flow effects in power system with high concentration of RDG. The analysis is conducted on a sample DN, i.e., IEEE 13-bus test system, with RDG by considering the most critical scenario such as low power demand and peak power injection to DN from RDG. The Simulations are carried out using MATLAB/Simulink software, a mathematical model of a distribution grid, integrating RDG is developed for studying the effects of voltage rise and bidirectional flow of power. Furthermore, a control strategy is proposed to be installed at PCC of the DN to control/or mitigate the voltage rise effects and to limit the reverse power flow when operating in a worst critical scenario of minimum load and maximum generation from RDG. The proposed control strategy also mitigates the voltage-current harmonic signals, improve the power factor, and voltage stability at PCC. Finally, recommendations are provided for the utility and independent power producer to counteract the effects of voltage rise at PCC. The study demonstrated that, PCC voltage can be sustained with a high concentration of RDG during a worst-case scenario without a reverse power flow and voltage rise beyond grid code limits.

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“…The challenge for controlling the battery system and EV load in the system is finding the optimal power level depending on the previous and current solar generation and load as well as the future changes [16]. In this paper, we first generate solar power forecasts to predict the future changes in solar power output.…”

Section: Introductionmentioning

confidence: 99%

Solar Power Smoothing in a Nanogrid Testbed

Panamtash

Sun

York

et al. 2022

2022 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)

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High penetration of solar power introduces new challenges in the operation of distribution systems. Considering the highly volatile nature of solar power output due to changes in cloud coverage, maintaining the power balance and operating within ramp rate limits can be an issue. Great benefits can be brought to the grid by smoothing solar power output at individual sites equipped with flexible resources such as electrical vehicles and battery storage systems. This paper proposes several approaches to a solar smoothing application by utilizing battery storage and EV charging control in a "Nanogrid" testbed located at a utility in Florida. The control algorithms focus on both realtime application and predictive control depending on forecasts. The solar smoothing models are then compared using real data from the Nanogrid site to present the effectiveness of the proposed models and compare their results. Furthermore, the control methods are applied to the Orlando Utilities Commission (OUC) Nanogrid to confirm the simulation results.

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Predictive Coordinated and Cooperative Voltage Control for Systems With High Penetration of PV

Cited by 40 publications

References 30 publications

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

Analysis of Voltage Rise Phenomena in Electrical Power Network With High Concentration of Renewable Distributed Generations

Solar Power Smoothing in a Nanogrid Testbed

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