An expert system approach to the unit commitment problem

Kothari, D. P.; Ahmad, Aijaz

doi:10.1016/0196-8904(94)00075-b

Cited by 17 publications

(5 citation statements)

References 4 publications

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“…This operator [22] generates distorted vectors X i by disturbing a randomly chosen vector 'X a ' and dissimilarity randomly chosen vectors 'X b ' and 'X c ' as per the following equation:…”

Section: • Mutationmentioning

confidence: 99%

Optimization Methodologies and Testing on Standard Benchmark Functions of Load Frequency Control for Interconnected Multi Area Power System in Smart Grids

et al. 2020

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In the recent era, the need for modern smart grid system leads to the selection of optimized analysis and planning for power generation and management. Renewable sources like wind energy play a vital role to support the modern smart grid system. However, it requires a proper commitment for scheduling of generating units, which needs proper load frequency control and unit commitment problem. In this research area, a novel methodology has been suggested, named Harris hawks optimizer (HHO), to solve the frequency constraint issues. The suggested algorithm was tested and examined for several regular benchmark functions like unimodal, multi-modal, and fixed dimension to solve the numerical optimization problem. The comparison was carried out for various existing models and simulation results demonstrate that the projected algorithm illustrates better results towards load frequency control problem of smart grid arrangement as compared with existing optimization models.

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“…This operator [22] generates distorted vectors X i by disturbing a randomly chosen vector 'X a ' and dissimilarity randomly chosen vectors 'X b ' and 'X c ' as per the following equation:…”

Section: • Mutationmentioning

confidence: 99%

Optimization Methodologies and Testing on Standard Benchmark Functions of Load Frequency Control for Interconnected Multi Area Power System in Smart Grids

et al. 2020

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“…The unit commitment problem is also solved by many heuristic techniques. Kothari and Ahmad [35] proposed a method which combines dynamic programming with expert system and a rule-based system for solving the unit commitment problem and named this method as hybrid approach.…”

Section: Advantages and Disadvantagesmentioning

confidence: 99%

“…As unit commitment is a wide-ranging, combinational and mixed integer nonlinear programming problem [16][17][18][19]. Several optimization methods have been used to solve this type of problem including priority listing [20,21], mixed integer programming [22][23][24][25], dynamic programming [26][27][28][29][30][31][32][33], hierarchical optimization [34][35][36][37][38][39], lagrange relaxation method [40][41][42][43][44][45], tabu search [46][47][48], non-linear programming problem [49,50] and branch and bound method [51]. There are two families of unit commitment, one is deterministic unit commitment problem and the other is stochastic unit commitment problem.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Parvez

Shen

Khan

et al. 2019

Water

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The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

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“…There are key difficulties, to resolve the UC problems, by incorporating these classical approaches like deprived convergence, computation intricacy to handle multi-objective functions with many constraints, to achieve efficient results. Nontraditional artificial intelligence based optimization approaches like Network Programming (NP) [80], Tabu Search (TS) [81], Hybrid fuzzy based TS [82], Heuristic search techniques [83]- [85], Simulated Annealing (SA) [86]- [89], Twofold SA [90], [91], Adaptive SA [92], Enhanced SA [93], [94], Stochastic SA [95], [96], Ant Colony Optimization (ACO) [97], [98], ACO with Random Perturbation [99], Memory Bounded ACO [100], Nodal ACO [101], Hybrid Taguchi ACO [102], Fuzzy Logic [103]- [107], Fuzzy based SA [108], [109], Fuzzy DP [110], Fuzzy Hierarchical Bi-Level Modelling [111], Artificial Neural Network (ANN) [112]- [119], Hybrid ANN [120]- [122], Hopfield ANN [123]- [127], Expert System [128]- [131] and Quasi-Oppositional Teaching Learning Algorithm [132], could cope with the convergence properties, intricacy of computational operation and give innovative solutions against conservative methods. Every traditional and non-traditional technique has diverse properties, merits and demerits.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

Muralikrishnan¹,

Jebaraj²,

Rajan

2020

IEEE Access

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Unit Commitment (UC) is a key task in electric power system operation, aiming at minimizing the total cost of power generation. It is essential to monitor wide range of activities and practices of UC necessary to determine the operating plan of generating units. The UC problem is particularly crucial, when the behavior of loads at every hour interval, is oscillatory and with different operational constraints and environments. Many works have been proposed, with different optimization methods to solve the UC problem. This paper gives a detailed review of the evolutionary optimization techniques, employed for solving UC problem, by collecting them from lots of peer reviewed published papers. This review was carried out under many sections, based on various evolutionary optimization techniques, to help new researchers, dealing with modern UC problem solutions, under different situations of power system.

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An expert system approach to the unit commitment problem

Cited by 17 publications

References 4 publications

Optimization Methodologies and Testing on Standard Benchmark Functions of Load Frequency Control for Interconnected Multi Area Power System in Smart Grids

Optimization Methodologies and Testing on Standard Benchmark Functions of Load Frequency Control for Interconnected Multi Area Power System in Smart Grids

Modeling and Solution Techniques Used for Hydro Generation Scheduling

A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem

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