scholarly journals Transmission Congestion Management Considering Modeling of Solar Photovoltaic Distributed Generator in Deregulated Power System

2019 ◽  
Vol 8 (3) ◽  
pp. 2086-2093

This paper presents an effective methodology for transmission congestion management (TCM) in deregulated power system considering random nature of solar photovoltaic distributed generator (SPVDG). Solar photovoltaic power generation has gained popularity worldwide. Its’ optimal sitting in the grid can provide congestion relief and reduce line losses etc. However, to maximize the potential benefits of this renewable energy source, its’ stochastic power output which mainly depends on solar irradiance needs due consideration. In this paper, seasonal variations of solar irradiance have been modeled using beta probability density function to determine expected power output of SPVDG over various seasons of one year. TCM problem has been formulated as a non-linear programming with the objective of social welfare maximization of electricity market subject to equality and inequality constraints incorporating seasonal load demand variations. The optimal siting of SPVDG integration in the grid has been discussed. The proposed methodology has been simulated by incorporating practical data of a real-life SPVDG in standard IEEE 30-bus, IEEE 118-bus and practical Indian Utility 62-bus systems. Simulation results show the benefits of proposed methodology on market indices. The effectiveness of proposed approach is also discussed in comparison with existing methodology of distributed generation modeling

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.


Author(s):  
Kshitij Choudhary ◽  
Rahul Kumar ◽  
Dheeresh Upadhyay ◽  
Brijesh Singh

The present work deals with the economic rescheduling of the generation in an hour-ahead electricity market. The schedules of various generators in a power system have been optimizing according to active power demand bids by various load buses. In this work, various aspects of power system such as congestion management, voltage stabilization and loss minimization have also taken into consideration for the achievement of the goal. The interior point (IP) based optimal power flow (OPF) methodology has been used to obtain the optimal generation schedule for economic system operation. The IP based OPF methodology has been tested on a modified IEEE-30 bus system. The obtained test results shows that not only the generation cost is reduced also the performance of power system has been improved using proposed methodology.


Sign in / Sign up

Export Citation Format

Share Document