A fast technique for unit commitment problem by extended priority list

Senjyu, Tomonobu; Shimabukuro, K.; Uezato, K.; Funabashi, Toshihisa

doi:10.1109/tdc.2002.1178301

Cited by 215 publications

(69 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A great deal of money can be saved by turning units off (decommitting them) when they are not needed and thus make this optimization problem increasingly significant. Many mathematical methods have been developed to solve the UCP and mainly include priority list (PL) [5], dynamic programming (DP) [6], mixed-integer linear programming [7,8], branch-and-bound [9] and lagrangian relaxation (LR) [10]. Among these methods, the PL method is simple and fast but yields suboptimal solutions with higher operating cost due to limited search space.…”

Section: List Of Symbolsmentioning

confidence: 99%

“…Repairing the minimum up/down time constraint may lead to excessive power generation in any hour over the scheduling time horizon, which is not desirable due to high operating costs [5]. Therefore, the redundant units must be decommitted without violating the minimum up/down time and spinning reserve constraints.…”

Section: Unit Decommitment Strategy For Excessive Spinning Reservementioning

confidence: 99%

See 1 more Smart Citation

A Novel Strategy-Based Hybrid Binary Artificial Bee Colony Algorithm for Unit Commitment Problem

2015

View full text Add to dashboard Cite

In this paper, a hybrid approach based on a novel binary artificial bee colony (NBABC) algorithm and local search (LS) is developed to solve the unit commitment problem (UCP). Also, the ramp rate constraints are taken into account in the solution of UCP by performing conventional economic dispatch with modifying unit generating capacities over the entire scheduling time horizon. The proposed NBABC-LS method differs from its counterparts in three main aspects: (i) it utilizes a novel strategy which measures the dissimilarity between two binary strings for generating the new binary strings for UCP; (ii) it uses an intelligent scout bee phase; (iii) A LS module is hybridized with the NBABC algorithm. These modifications result in three major advantages: (i) it avoids the problem of slow and premature convergence and thus does not fall into the local optimum solutions; (ii) it can quickly find the near global optimum solution for UCP; (iii) accuracy and robustness of the solution are achieved. The proposed approach is successfully applied to the test systems up to 100 thermal units over 24-h scheduling time horizon and the real Turkish interconnected power system consisting of eight thermal units over 8-h scheduling time horizon. The obtained results confirm the quality solution in terms of total generation cost compared with the other methods reported in the literature.

show abstract

Section: List Of Symbolsmentioning

confidence: 99%

Section: Unit Decommitment Strategy For Excessive Spinning Reservementioning

confidence: 99%

A Novel Strategy-Based Hybrid Binary Artificial Bee Colony Algorithm for Unit Commitment Problem

2015

View full text Add to dashboard Cite

show abstract

“…To assess the effi ciency of the BDE, ten-unit system is used as test system, where it is widely used in the literature [5][6][7], [15], [18][19][20][21][22][23], [27][28][29][30][31][32][33][34]. Also, in order to fi nd the effectiveness and superiority of the BDE algorithm, the test results are compared with the results available in [5][6][7], [15], [18][19][20][21][22][23], [27][28][29][30][31][32][33][34].…”

Section: Study System and Implementation Of Bdementioning

confidence: 99%

“…Also, in order to fi nd the effectiveness and superiority of the BDE algorithm, the test results are compared with the results available in [5][6][7], [15], [18][19][20][21][22][23], [27][28][29][30][31][32][33][34].…”

Section: Study System and Implementation Of Bdementioning

confidence: 99%

See 1 more Smart Citation

Unit commitment scheduling using binary differential evolution algorithm

2009

View full text Add to dashboard Cite

This paper presents a new approach for thermal generating units scheduling using binary differential evolution (BDE) algorithm. Solving the unit commitment (UC) problem by BDE is a two stage process. In the fi rst stage, the economic dispatch of the units are solved and hourly optimum solution of UC is obtained considering all constraints except the minimum up time (MUT) and minimum down time (MDT) constraints. In the second stage, the MUT and MDT are enforced by defi ning a probability function followed by modifying the schedule obtained in the fi rst step. To validate the results obtained by BDE, a version of genetic algorithm (GA), namely as, PNUCO is applied for comparison. Also, the results obtained by BDE and PNUCO are compared with the previous approaches reported in the literature. The results show that the BDE produces optimal or nearly optimal solutions for the study systems

show abstract

A novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment

Haq

Ahmad

Ikram

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary For an effective and reliable power system, determining the optimal state of the accessible producing units is the key step, which comprises of two segments; making decision in committing the generators (unit commitment) followed by the decision‐making of their active power allocation (economic dispatch). Unit commitment (UC) problem is a mixed‐integer bit transition‐state optimization problem to which classical optimizing tools strive to evaluate, whereas metaheuristic methods prove to be a healthy attempt. In this study, a binary model of the polar bear optimization (BPBO) algorithm is developed and is utilized to solve this combinatorial problem. Polar bear optimization (PBO) is a newly developed swarm‐based approach, simulating three mechanisms in one technique in the form of effectual exploration and exploitation phase along with dynamic control of population size. BPBO uses a modified sigmoid conversion function to produce the state bits (ON/OFF) of the units. These three potent procedures help in determining the optimal transition state of units by varying the size of the search agents. For solving the economic dispatch problem (EDP), the conventional lambda iteration method is used. Repairing of the infeasible bit is also carried out by applying heuristic adjustments. To illustrate the vitality of the BPBO, it is modeled in “MATLAB 2016a” and tested on IEEE 4, 5, 10, 20, 40, 60, 80, and 100‐unit test systems. Comparative evaluation in terms of optimum combinations, active power allocation, and simulation time with other available methodologies is also done, resulting in a progressive approach with superiority to other recent methods.

show abstract

A fast technique for unit commitment problem by extended priority list

Cited by 215 publications

References 8 publications

A Novel Strategy-Based Hybrid Binary Artificial Bee Colony Algorithm for Unit Commitment Problem

A Novel Strategy-Based Hybrid Binary Artificial Bee Colony Algorithm for Unit Commitment Problem

Unit commitment scheduling using binary differential evolution algorithm

A novel binary variant model of swarm inspired polar bear optimization algorithm employed for scalable unit commitment

Contact Info

Product

Resources

About