Stochastic modeling to represent wind power generation and demand in electric power system based on real data

Verdejo, Humberto; Awerkin, Almendra; Saavedra, Eugenio; Kliemann, Wolfgang; Vargas, Luis

doi:10.1016/j.apenergy.2016.04.004

Cited by 62 publications

(43 citation statements)

References 35 publications

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“…Met Data is the data set recording several quantities related to wind such as velocity, speed, and temperature at the towers named M2, M4, and M5 with recording facilities in some altitudes. The statistical modeling for wind data with stochastic differential equations has gathered interest: Bensoussan and Brouste () fit the Cox–Ingersoll–Ross model to wind speed data and reported that the Cox–Ingersoll–Ross model overwhelms other methods, such as static models, in terms of prediction precision; Verdejo, Awerkin, Saavedra, Kliemann, and Vargasd ) modeled both power generation by windmills and power demand with Ornstein–Uhlenbeck processes with some preprocessing and examined their performances for practical purposes. We especially focus on the two‐dimensional data with 0.05‐s resolution representing wind velocity labeled Sonic x and Sonic y (119M) at the M5 tower, from 00:00:00 on July 1, 2017 to 20:00:00 on July 5, 2017 (see Figures and ).…”

Section: Real Data Analysis: Met Data Of Nwtcmentioning

confidence: 99%

Inference for ergodic diffusions plus noise

Nakakita

Uchida

2018

Scandinavian J Statistics

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We research an adaptive maximum‐likelihood–type estimation for an ergodic diffusion process where the observation is contaminated by noise. This methodology leads to the asymptotic independence of the estimators for the variance of observation noise, the diffusion parameter, and the drift one of the latent diffusion process. Moreover, it can lessen the computational burden compared to simultaneous maximum likelihood–type estimation. In addition to adaptive estimation, we propose a test to see if noise exists or not and analyze real data as the example such that the data contain observation noise with statistical significance.

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Section: Real Data Analysis: Met Data Of Nwtcmentioning

confidence: 99%

Inference for ergodic diffusions plus noise

Nakakita

Uchida

2018

Scandinavian J Statistics

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“…A low‐turbulence stochastic wind speed that varies between 7 and 11 m/second is investigated to mimic a general wind variation . The corresponding PMSG responses are demonstrated by Figure , from which it can observe that the power coefficient of AFOPID control is the closest to the optimum among all controllers, such that it can extract the maximum power from wind.…”

Section: Case Studiesmentioning

confidence: 99%

“…where PID control gains K P1 , K P2 , K I1 , K I2 , K D1 , and K D2 , fractional integrator order μ 1 and μ 2 , and differentiator order λ 1 and λ 2 are chosen to realize a satisfactory convergence of tracking error dynamics (20). The AFOPID control parameters in Equation 31 and observer gains in Equations 29 and 30 are optimally tuned through PSO 29 under 3 cases, eg, (a) step change of wind speed, (b) low-turbulence stochastic wind speed, and (c) high-turbulence stochastic wind speed. The optimization goal is to minimize the tracking error of mechanical rotation speed and d-axis current, together with the corresponding control costs, which model is given as follows…”

Section: Afopid Control Design Of Pmsg For Mpptmentioning

confidence: 99%

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Yang

Shu

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper designs a novel adaptive fractional‐order PID (AFOPID) control of a permanent magnetic synchronous generator (PMSG)‐based wind energy conversion system, which attempts to extract the maximum wind power by using a linear perturbation observer. The combinatorial effect of generator nonlinearities and parameter uncertainties, unmodelled dynamics, and stochastic wind speed variation is aggregated into a perturbation, which is then estimated in real time by a linear extended‐state observer called high‐gain state and perturbation observer. Besides, the perturbation estimate is used as an auxiliary control signal which is fully compensated by a fractional‐order PID (FOPID) controller to achieve a globally robust control consistency, simple structure and high reliability, as well as an improved tracking performance compared to that of PID control. In addition, AFOPID does not require an accurate PMSG model while only the measurement of d‐axis current and mechanical rotation speed is required, in which parameter is optimally tuned by particle swarm optimization. Four case studies are carried out, including step change of wind speed, low‐turbulence stochastic wind speed, high‐turbulence stochastic wind speed, and generator parameter uncertainties, respectively. Simulation results verify the effectiveness and superiority of AFOPID compared to that of PID, FOPID, and nonlinear control.

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“…Nowadays, the ever increasing electrification of transportation and buildings may indeed increase the demand fluctuations, putting a strain on the system stability [10], [11]. For this reason, the resilience and reliability of the power grid may benefit from the design and analysis of control strategies that theoretically guarantee the system stability in presence of timevarying loads [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Output Regulation for Voltage Control in DC Networks With Time-Varying Loads

Silani

Cucuzzella

Scherpen

et al. 2021

IEEE Control Syst. Lett.

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In this letter, we propose a novel control scheme for regulating the voltage in Direct Current (DC) networks. More precisely, the proposed control scheme is based on the output regulation methodology and, differently from the results in the literature, where the loads are assumed to be constant, we consider time-varying loads whose dynamics are described by a class of nonlinear differential equations. We prove that the proposed control scheme achieves voltage regulation ensuring the stability of the overall network.

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Stochastic modeling to represent wind power generation and demand in electric power system based on real data

Cited by 62 publications

References 35 publications

Inference for ergodic diffusions plus noise

Inference for ergodic diffusions plus noise

Adaptive fractional-order PID control of PMSG-based wind energy conversion system for MPPT using linear observers

Output Regulation for Voltage Control in DC Networks With Time-Varying Loads

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