Profit based unit commitment using hybrid optimization technique

Anand, Himanshu; Narang, Nitin; Dhillon, J.S.

doi:10.1016/j.energy.2018.01.138

Cited by 47 publications

(20 citation statements)

References 37 publications

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“…The statistical analysis of the proposed and the existing approaches is delineated in table 3. This analysis compares the mean, median and standard deviation of the proposed technique with the existing research work such as moth flame optimization, cuckoo search, binary successive approach and civilized swarm optimization (Anand et al, 2018), binary sine-cosine algorithm (Reddy et al, 2017), binary grey wolf optimizer (Reddy et al,2019), ant colony optimization (Vaisakh and Srinivas, 2011), nodal ant colony optimization (Columbus et al, 2012), Parallel artificial bee colony (Christopher Columbus and Simon, 2012), parallel nodal ant colony optimization (Christopher Columbus and Simon, 2013) and binary fireworks algorithm based (Srikanth Reddy et al, 2016) on the profit based unit commitment.…”

Section: Resultsmentioning

confidence: 99%

A mutual approach for profit-based unit commitment in deregulated power system integrated with renewable energy sources

Anbazhagi

Asokan

Ashokkumar

2020

Transactions of the Institute of Measurement and Control

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This paper proposes a mutual technique for solving the profit-based unit commitment (PBUC) problem in deregulated power system integrated with wind power. The proposed mutual approach is the joined execution of different solution techniques and known by the non-dominated sorting of moth fly optimization (MFO) with levy flight search (NSMFLF) technique. In the proposed approach, the levy flight search and the traditional moth flame optimization looking conduct is prepared in parallel as for the objective function and update the conceivable combination of generation units. The objective function maximizes the profit of the generating companies as for the revenue and total fuel cost in light of the gauge estimations of power demand, price and reserve power. Here, the uncertainty events of the wind power are predicted by utilizing the artificial intelligence techniques. Thus, the system is ensured with the high utilization of wind power. Finally, the non-dominated sorting is performed to choose the optimal solution from the conceivable generated combinations. The optimal combination used to maximize the profit of the generating companies and solve the PBUC problem in light of the objective function. The proposed method is implemented in the matrix laboratory working stage and the outcomes are analyzed with the current strategies.

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Section: Resultsmentioning

confidence: 99%

A mutual approach for profit-based unit commitment in deregulated power system integrated with renewable energy sources

Anbazhagi

Asokan

Ashokkumar

2020

Transactions of the Institute of Measurement and Control

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“…The convergence rate of NBABC-LS and NBABC-GC was found to be faster than NBABC. A hybrid approach based on the integration of civilized swarm optimization (CSO) and binary successive approach (BSA) has been suggested to resolve profit-based unit commitment problem on 10, 40 and 100 units by (Anand et al, 2018) and the tuneable parameters considered in this work are number of societies ( N s ), number of members in each society ( N m ), inertia weight ( w ), maximum iteration ( k max ) and acceleration coefficients of civilization leader ( C L ), society leader ( C SL1 and C SL2 ) and society members ( C SM1 and C SM2 ). From the analysis researchers concluded that BSA is used for exploring optimal status in less time while CSO investigates optimal generation schedule using the obtained optimal status.…”

Section: Uc Solution Techniquesmentioning

confidence: 99%

Investigation of solution techniques of unit commitment problems: A review

2021

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The Unit Commitment (UC) is a significant act of optimization in day-to-day operational planning of modern power systems. After load forecasting, UC is the subsequent step in the planning process. The electric utilities decide in advance which units are to start-up, when to connect them to the network, the sequence in which the generating units should be shut down and for how long. In view of the above, this paper attempts on presenting a thorough and precise review of the recent approaches applied in optimizing UC problems, incorporating both stochastic and deterministic loads, based on various peer reviewed published research papers of reputed journals. It emphasizes on non-conventional energy and distributed power generating systems along with deregulated and regulated environment. Along with an overview, a comprehensive analysis of the UC algorithms reported in the recent past since 2015 has been discussed for the assistance of new researchers concerned with this domain.

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“…Hybrid algorithms normally give better optimal results. Some of the efficiently deployed hybrid metaheuristic algorithms in existing literature are the neural-network-based tabu search (NBTS) [89], GA and differential evolution (DE) [90], simulated annealing-based (EP) [91], PSO and EP [92], binary successive approach (BSA) and civilized swarm optimization (CSO) [93], and binary particle swarm optimization (BPSO) and PSO [94].…”

Section: Overview Of Algorithms For Solving Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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Profit based unit commitment using hybrid optimization technique

Cited by 47 publications

References 37 publications

A mutual approach for profit-based unit commitment in deregulated power system integrated with renewable energy sources

A mutual approach for profit-based unit commitment in deregulated power system integrated with renewable energy sources

Investigation of solution techniques of unit commitment problems: A review

Energy Storage System Analysis Review for Optimal Unit Commitment

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