Optimal power flow using artificial bees algorithm

2010 ◽  
Author(s):  
Umaporn Kwannetr ◽  
Uthen Leeton ◽  
Thanatchai Kulworawanichpong
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Bees Algorithm ◽  
Optimal Power

Bees Two-Hive Algorithm for Optimal Power Flow

2013 ◽  
Vol 313-314 ◽  
pp. 870-875
Author(s):  
Nopbhorn Leeprechanon ◽  
Prakornchai Phonrattanasak
Keyword(s):  
Energy Balance ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
Bees Algorithm ◽  
Fuel Cost ◽  
Physical Constraints ◽  
Optimal Power ◽  
Transmission Limits ◽  
Simulation Results

This paper presents bees two-hive algorithm for solving the optimal power flow (OPF) problem with various constraints. The objective of the proposed technique is to improve the quality solution of the conventional bees algorithm that minimize the total fuel cost subject to operational and physical constraints i.e. energy balance, generation and transmission limits including security constraints. The proposed methodology is tested on the IEEE 30-bus test system. The results obtained using the proposed approach are compared to GA, PSO, BA and other conventional. The comparison of quality solution with other algorithms confirms performance of proposed technique. Simulation results demonstrate that bees two-hive algorithm provides better results than other heuristic techniques.

Optimal Power Flow Using Newton’s Method

2012 ◽  
Vol 3 (2) ◽  
pp. 167-169
Author(s):  
F.M.PATEL F.M.PATEL ◽  
◽  
N. B. PANCHAL N. B. PANCHAL
Keyword(s):  
Newton’S Method ◽  
Newton's Method ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Optimal Power

Designing the Electricity Market with Energy Storage Devices

2020 ◽  
Vol 12 (12) ◽  
pp. 31-43
Author(s):  
Tatiana A. VASKOVSKAYA ◽  
◽  
Boris A. KLUS ◽  
Keyword(s):  
Energy Storage ◽  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Storage Systems ◽  
Energy System ◽  
Energy Storage Systems ◽  
Storage Model ◽  
Optimal Power ◽  
Wide Range

The development of energy storage systems allows us to consider their usage for load profile leveling during operational planning on electricity markets. The paper proposes and analyses an application of an energy storage model to the electricity market in Russia with the focus on the day ahead market. We consider bidding, energy storage constraints for an optimal power flow problem, and locational marginal pricing. We show that the largest effect for the market and for the energy storage system would be gained by integration of the energy storage model into the market’s optimization models. The proposed theory has been tested on the optimal power flow model of the day ahead market in Russia of 10000-node Unified Energy System. It is shown that energy storage systems are in demand with a wide range of efficiencies and cycle costs.

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