Two-Point Estimate Method for Probabilistic Optimal Power Flow Computation Including Wind Farms with Correlated Parameters

This paper presents a genetic algorithm (GA) based approach for the solution of the optimal power flow (OPF) in distribution networks with distributed generation (DG) units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs) of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators.

Download Full-text

A new point estimate method for probabilistic load flow with correlated variables including wind farms

2014 IEEE PES General Meeting | Conference & Exposition ◽

10.1109/pesgm.2014.6939141 ◽

2014 ◽

Author(s):

Can Chen ◽

Wenchuan Wu ◽

Boming Zhang ◽

Chanan Singh

Keyword(s):

Wind Farms ◽

Load Flow ◽

Point Estimate ◽

Correlated Variables ◽

Probabilistic Load Flow ◽

Point Estimate Method ◽

Estimate Method ◽

Probabilistic Load

Download Full-text

Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

Sustainability ◽

10.3390/su12020518 ◽

2020 ◽

Vol 12 (2) ◽

pp. 518

Author(s):

Yue Chen ◽

Zhizhong Guo ◽

Hongbo Li ◽

Yi Yang ◽

Abebe Tilahun Tadie ◽

...

Keyword(s):

Power Systems ◽

Power Flow ◽

Optimal Power Flow ◽

Test System ◽

Power Sources ◽

Renewable Power ◽

Point Estimate Method ◽

Optimal Power ◽

Simulation Results ◽

Point Estimation Method

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

Download Full-text

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

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.18653 ◽

2018 ◽

Vol 7 (4) ◽

pp. 2766 ◽

Cited By ~ 1

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.

Download Full-text