Probabilistic optimal power flow considering dependences of wind speed among wind farms by pair-copula method

2017 ◽  
Vol 84 ◽  
pp. 296-307 ◽  
Author(s):  
Jia Cao ◽  
Zheng Yan
Keyword(s):  
Wind Speed ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Wind Farms ◽  
Optimal Power ◽  
Copula Method

scholarly journals Optimal Power Flow Solution of Wind-Integrated Power System Using Novel Metaheuristic Method

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6117
Author(s):  
Amr Khaled Khamees ◽  
Almoataz Y. Abdelaziz ◽  
Makram R. Eskaros ◽  
Adel El-Shahat ◽  
Mahmoud A. Attia
Keyword(s):  
Wind Speed ◽  
Power System ◽  
Wind Energy ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Wind Farm ◽  
Wind Farms ◽  
Energy System ◽  
Optimal Power ◽  
Metaheuristic Methods

Wind energy is particularly significant in the power system today since it is a cheap and clean power source. The unpredictability of wind speed leads to uncertainty in devolved power which increases the difficulty in wind energy system operation. This paper presents a stochastic optimal power flow (SCOPF) for obtaining the best scheduled power from wind farms while lowering total operational costs. A novel metaheuristics method called Aquila Optimizer (AO) is used to address the SCOPF problem due to its highly nonconvex and nonlinear nature. Wind speed is represented by the Weibull probability distribution function (PDF), which is used to anticipate the cost of wind-generated power from a wind farm based on scheduled power. Weibull parameters that provide the best match to wind data are estimated using the AO approach. The suggested wind generation cost model includes the opportunity costs of wind power underestimation and overestimation. Three IEEE systems (30, 57, and 118) are utilized to solve optimal power flow (OPF) using the AO method to prove the accuracy of this method, and results are compared with other metaheuristic methods. With six scenarios for the penalty and reverse cost coefficients, SCOPF is applied to a modified IEEE-30 bus system with two wind farms to obtain the optimal scheduled power from the two wind farms which reduces total operation cost.

scholarly journals Multi-objective optimal power flow in the presence of intermittent renewable energy sources

2018 ◽  
Vol 7 (4) ◽  
pp. 2766 ◽  
Author(s):  
S. Surender Reddy
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Wind Farms ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function. In this paper, three objective functions i.e., total generation cost, transmission losses and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the MO-OPF problems are solved using the multi-objective glowworm swarm optimiza-tion (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 bus system with two wind farms located at two different buses in the system.  

scholarly journals Optimal Management of a Microgrid with Radiation and Wind-Speed Forecasting: A Case Study Applied to a Bioclimatic Building

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2398
Author(s):  
Luis O. Polanco Vásquez ◽  
Víctor M. Ramírez ◽  
Diego Langarica Córdova ◽  
Juana López Redondo ◽  
José Domingo Álvarez ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Energy Management ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Photovoltaic System ◽  
Management Tool ◽  
Optimal Power ◽  
Double Exponential Smoothing ◽  
The University

An Energy Management System (EMS) that uses a Model Predictive Control (MPC) to manage the flow of the microgrids is described in this work. The EMS integrates both wind speed and solar radiation predictors by using a time series to perform the primary grid forecasts. At each sampling data measurement, the power of the photovoltaic system and wind turbine are predicted. Then, the MPC algorithm uses those predictions to obtain the optimal power flows of the microgrid elements and the main network. In this work, three time-series predictors are analyzed. As the results will show, the MPC strategy becomes a powerful energy management tool when it is integrated with the Double Exponential Smoothing (DES) predictor. This new scheme of integrating the DES method with an MPC presents a good management response in real-time and overcomes the results provided by the Optimal Power Flow method, which was previously proposed in the literature. For the case studies, the test microgrid located in the CIESOL bioclimatic building of the University of Almeria (Spain) is used.

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