A comparison of Gumbel and Weibull statistical models to estimate wind speed for wind power generation

Summary Integration of renewable energy sources provides energy security, substantial cost savings, and reduction in greenhouse gas emissions, enabling nation to meet emission targets. Microgrid energy management (MGEM) is a challenging task for microgrid operator (MGO) for optimal energy utilization with penetration of renewable energy sources, energy storage devices, and demand response. In this paper, optimal energy dispatch strategy is established for grid‐connected and stand‐alone microgrids integrated with photovoltaic, wind turbine, fuel cell, microturbine, diesel generator, and battery storage system. Techno‐economic benefits are demonstrated for the hybrid power system considering uncertainty of load and generation. So far, MGEM problem has been addressed with the aim of minimizing operating cost only. However, the issues of power losses and emission‐related objectives need to be addressed for effective MGEM. In this paper, MGEM is formulated as mixed‐integer linear programming, and a new multi‐objective solution is proposed for MGEM. Demand response is also included in the optimization problem to demonstrate its impact on optimal energy dispatch and techno‐commercial benefits. Simulation results are obtained for the optimal capacity of each component, charging/discharging scheduling, state of charge, power import from main grid, net present cost, cost of energy, initial cost, operational cost, fuel cost, and cost of emissions taking care of seasonal load variation throughout the year. The results show that CO2 emission in stand‐alone microgrid is reduced by 41.74% compared with traditional system with diesel generator. Simulation results obtained with the proposed method has been compared with various evolutionary algorithms to verify its effectiveness.

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“…In this work, uncertainty of PV and WT power output is modelled using beta distribution function and Weibull distribution function, respectively.

f_{b} ((), s) = \frac{s^{α - 1} {(1 - s)}^{β - 1}}{Γ ((), α) . Γ ((), β)} Γ ((), α + β),

α = μ ((), \frac{((), 1 - μ) μ}{σ} - 1),

β = ((), 1 - μ false) false(\frac{((), 1 - μ) μ}{σ} - 1),

P_{PV} = ({, \begin{matrix} P_{italicPV_italicrated} * \frac{s}{{normals}_{normalr}}, 0 < s < s_{r} \\ P_{italicPV_italicrated} s_{r} < s \end{matrix}),

f ((), v) = \frac{k}{c} {(\frac{v}{c})}^{k - 1} italicexp {(\frac{- v}{c})}^{k}, 0 \leq v \leq \infty

k = {(\frac{σ}{\overset{false¯}{v}})}^{- 1.086},

c = \frac{\overset{false¯}{v}}{Γ ((), 1 + \frac{1}{k}) .}

…”

Section: Modelling Of Hybrid Energy Sourcesmentioning

confidence: 99%

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

Murty

Kumar

2020

Int Trans Electr Energ Syst

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“…In [21], WT and PV-based DG units were allocated in time-varying distribution networks by carrying out probabilistic load flow (PLF) analysis, Monte Carlo simulation (MCS) and the point estimate methods. Comparison between the Weibull and Gumbel probability distribution functions (PDF) was presented in [22] to predict wind speed and wind power output considering DG in distribution systems. PLF was presented in [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Determining penetration limit of central PVDG topology considering the stochastic behaviour of PV generation and loads to reduce power losses and improve voltage profiles

Suliman

Hizam

Othman

2020

IET Renewable Power Generation

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“…The random behaviour of the wind speed can be defined by the Weibull distribution function. The velocity of wind is described mathematically as [23]. The parameters of the Weibull distribution, namely the shape and scale parameters are determined using historical wind speed data available.…”

Section: Wind Uncertainty Modellingmentioning

confidence: 99%

“…The Monte Carlo simulation (MCS) and the point estimate methods were used to carry out probabilistic load flow analysis. A comparison between the Weibull and Gumbel probability distribution functions (PDFs) was presented in [23] to predict wind speed and wind power output. In [24], the distribution system was studied considering various uncertainties in the presence of distribution generation.…”

Section: Introductionmentioning

confidence: 99%

Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

Murty

Kumar

2019

IET Smart Grid

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The potential availability of renewable energy sources is unquestionable and the government is setting steep targets for renewable energy usage. Renewable-based DGs, reduce dependence on fossil fuels, mitigate global climate change, ensure energy security, and reduce emissions of CO 2 and other greenhouse gases. This study addresses microgrid system analysis with hybrid energy sources and reconfiguration simultaneously for efficient operation of the system. Microgrid zones are formulated categorically with the existing distribution system. In this study, wind, solar and small hydro-based DGs are considered. Uncertainties of renewable power generation and load are also taken care in the optimization problem. A multiobjective optimisation method proposed in this paper for optimal integration of renewable-based DGs and reconfiguration of the network to minimise power loss and maximise annual cost savings. Optimal location and sizes of DG units are determined using gravitational search algorithm and general algebraic modelling system respectively. Optimal reconfiguration of the microgrid system is obtained using genetic algorithm. Simulation results are obtained for the IEEE 33-bus system and compared with existing methods as available in the literature. Furthermore, this study has been carried out with a 24-hr time-varying distribution system. The simulation results show the efficiency and accuracy of the proposed technique.

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A comparison of Gumbel and Weibull statistical models to estimate wind speed for wind power generation

Cited by 15 publications

References 4 publications

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

Retracted: Optimal energy dispatch in microgrids with renewable energy sources and demand response

Determining penetration limit of central PVDG topology considering the stochastic behaviour of PV generation and loads to reduce power losses and improve voltage profiles

Optimal DG integration and network reconfiguration in microgrid system with realistic time varying load model using hybrid optimisation

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