Probabilistic Optimal Power Flow in Electricity Markets Based on a Two-Point Estimate Method

2006 ◽  
Vol 21 (4) ◽  
pp. 1883-1893 ◽  
Author(s):  
G. Verbic ◽  
C.A. Canizares
Keyword(s):  
Electricity Markets ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Point Estimate ◽  
Point Estimate Method ◽  
Optimal Power ◽  
Estimate Method

scholarly journals Optimal power flow for distribution networks with distributed generation

2015 ◽  
Vol 12 (2) ◽  
pp. 145-170 ◽  
Author(s):  
Jordan Radosavljevic ◽  
Miroljub Jevtic ◽  
Dardan Klimenta ◽  
Nebojsa Arsic
Keyword(s):  
Distributed Generation ◽  
Wind Turbines ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Distribution Networks ◽  
Point Estimate ◽  
Photovoltaic Systems ◽  
Multi Objective ◽  
Point Estimate Method ◽  
Optimal Power

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.

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

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

scholarly journals An integrated OPF dispatching model with wind power and demand response for day-ahead markets

2019 ◽  
Vol 9 (4) ◽  
pp. 2794
Author(s):  
Ricardo Moreno ◽  
Johan Obando ◽  
Gabriel Gonzalez
Keyword(s):  
Wind Power ◽  
Demand Response ◽  
Electricity Markets ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Mixed Integer ◽  
New Paradigm ◽  
Linear Quadratic ◽  
Distributed Resources ◽  
Optimal Power

In the day-ahead dispatching of network-constrained electricity markets, renewable energy and distributed resources are dispatched together with conventional generation. The uncertainty and volatility associated to renewable resources represents a new paradigm to be faced for power system operation. Moreover, in various electricity markets there are mechanisms to allow the demand participation through demand response (DR) strategies. Under operational and economic restrictions, the operator each day, or even in intra-day markets, dispatchs an optimal power flow to find a feasible state of operation. The operation decisions in power markets use an optimal power flow considering unit commitment to dispatch economically generation and DR resources under security restrictions. This paper constructs a model to include demand response in the optimal power flow under wind power uncertainty. The model is formulated as a mixed-integer linear quadratic problem and evaluated through Monte-Carlo simulations. A large number of scenarios around a trajectory bid captures the uncertainty in wind power forecasting. The proposed integrated OPF model is tested on the standard IEEE 39-bus system.

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