PV based distributed generation power system protection: A review

Walt, Hermanus L.R. van der; Bansal, Ramesh C.; Naidoo, Vinogran

doi:10.1016/j.ref.2017.12.002

Cited by 45 publications

(27 citation statements)

References 39 publications

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“…Subject to (P GD t C GD t (22) in which w represents the weighting factor for concerning the risk, P PV t represents the total generated power from PV, and D t is the power demand at each time step t. The constraint (17) specifies that the PV's overall produced power is directed to the demand of the load. The constraint represented by Equation (18) represents the power demand which is entirely provided by the grid, ESS, or PV.…”

Section: Optimal Energy Management Based On the Minimization Of The Cmentioning

confidence: 99%

“…This may result in operating the power system near the maximum loading point [13] that, by increasing the negative impacts of end-users on producing emissions [14,15] and endangering the voltage stability, decreases the system flexibility [16]. As a consequence, several requirements became necessary for a flexible power system due to the expansion and use of renewable energy sources in an effective way [17,18]. This will lead to an increase in microgrids with the purpose of enabling bidirectional power flow between power generation and final customers which is expected to play a key role in future power networks.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Management of an Energy Storage Unit in a PV-Based Microgrid Integrating Uncertainty and Risk

et al. 2019

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This paper addresses an optimized management of a storage energy battery which is part of a microgrid with a connection to the main grid and is supplied by a photovoltaic (PV) power plant. The main contribution of this paper is to consider uncertainty in electricity price while managing the battery storage. The forecasted value for demand and PV unit are predicted by a seasonal autoregressive integrated moving average model (SARIMA)—capable of accurately characterizing both seasonality effects and tail fatness. The optimal operation of the battery is determined by resolving a linear optimization program in which the objective function comprises the conditional value at risk (CVaR). Using CVaR ensures that the demand is fully supplied while minimizing the risk and operational cost. The cost function is the difference between power sold and bought subject to the charging and discharging rates for the battery and defining upper and lower bounds for the level of battery charge. The simulation results confirm that the risk consideration has a significant effect on the optimized management of a storage energy battery in a photovoltaic grid-connected microgrid.

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Section: Optimal Energy Management Based On the Minimization Of The Cmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Management of an Energy Storage Unit in a PV-Based Microgrid Integrating Uncertainty and Risk

et al. 2019

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“…Renewable energy systems in recent years have been considered within distributed generation systems [4]. One of the advantages of renewable energy systems is that they can be configured in various system sizes to meet the local energy demand [5].…”

Section: Introductionmentioning

confidence: 99%

Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

Tran

Smith

2019

Energies

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Stochastic optimization of a district energy system (DES) is investigated with renewable energy systems integration and uncertainty analysis to meet all three major types of energy consumption: electricity, heating, and cooling. A district of buildings on the campus of the University of Utah is used as a case study for the analysis. The proposed DES incorporates solar photovoltaics (PV) and wind turbines for power generation along with using the existing electrical grid. A combined heat and power (CHP) system provides the DES with power generation and thermal energy for heating. Natural gas boilers supply the remaining heating demand and electricity is used to run all of the cooling equipment. A Monte Carlo study is used to analyze the stochastic power generation from the renewable energy resources in the DES. The optimization of the DES is performed with the Particle Swarm Optimization (PSO) algorithm based on a day-ahead model. The objective of the optimization is to minimize the operating cost of the DES. The results of the study suggest that the proposed DES can achieve operating cost reductions (approximately 10% reduction with respect to the current system). The uncertainty of energy loads and power generation from renewable energy resources heavily affects the operating cost. The statistical approach shows the potential to identify probable operating costs at different time periods, which can be useful for facility managers to evaluate the operating costs of their DES.

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“…For all methods, increasing the operational reliability of PV systems with low grid cost and detecting the causes of disturbance in PV systems are important. A review of these techniques can be found in [6][7][8][9]. For example, rate of change of frequency (ROCOF) [10], the vector surge method [11], real-valued negative colon selection [12], under/over voltage (U/OV) [13], the high impedance method [14], the hypothesis testing method [15], and external events vectors [16] have been presented as traditional protection techniques in PV systems.…”

Section: Introductionmentioning

confidence: 99%

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

et al. 2019

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A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

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PV based distributed generation power system protection: A review

Cited by 45 publications

References 39 publications

Optimal Management of an Energy Storage Unit in a PV-Based Microgrid Integrating Uncertainty and Risk

Optimal Management of an Energy Storage Unit in a PV-Based Microgrid Integrating Uncertainty and Risk

Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

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