A review of different optimisation techniques for solving single and multi-objective optimisation problem in power system and mostly unit commitment problem

Ananth, D. V. N.; Vineela, K. S. T.

doi:10.1080/01430750.2019.1611632

Cited by 10 publications

(5 citation statements)

References 197 publications

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“…The objective of Deterministic Optimal Power Flow (OPF) is to minimize a chosen objective function by adjusting the control variables optimally, considering a set of equality and inequality constraints. In general, the formulation of the OPF problem can be outlined as follows, as described in references [16][17][18][19][20][21][22][23][24][25][26]:…”

Section: Mathematical Modeling Of Distribution Power Flowmentioning

confidence: 99%

Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization

Tentu,

Kundala,

Darimireddy

2024

Preprint

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The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

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Section: Mathematical Modeling Of Distribution Power Flowmentioning

confidence: 99%

Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization

Tentu,

Kundala,

Darimireddy

2024

Preprint

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“…Both deterministic and meta-heuristic techniques inspired by nature are employed to solve the UCP [4]. Deterministic techniques including Lagrangian relaxation (LR) [5][6][7], priority list [8,9], mixed-integer programming (MIP) [10] and dynamic programming (DP) [11,12] belong to numerical optimization techniques, which are considered the classical methods and have the advantages of simplicity and fast convergence, but mostly suffer from poor solution quality and premature convergence.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

et al. 2021

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Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.

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“…All these techniques are invented and developed in different periods by various researchers to solve optimization problems. The work in [1][2][3][4][5] provide the development of various solution methods, unit commitment problem formation, and historical study of the UCP during different period of time. A detailed survey on unit commitment is suggested in [1] along with its solution techniques.…”

Section: Introductionmentioning

confidence: 99%

“…A comparative study of various solution methods is suggested in [3] along with a detailed study of UCP. The various kinds of solution techniques discussed in detail [4][5] are used to solve the UCP in various works. A combined approach of the modified priority list (MPL) method and the sequential approach with matrix framework (SAMF) are used to solve the UCP [6] along with the economic dispatch approach for thermal units.…”

Section: Introductionmentioning

confidence: 99%

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

Singh

Mani

Singh

et al. 2023

IJRITCC

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The unit commitment problem is a complex and essential problem in the power generation field, which is solved to obtain the schedule of a large number of generating units to minimize the operating cost and the fulfillment of consumer load demand. The present work solves the unit commitment problem using quantum-inspired evolutionary algorithms with a novel elitist local search method (QIEA-ELS). The proposed algorithm solves the unit commitment problem efficiently and its applicability is verified on various unit test systems. The constraints are satisfied efficiently to find a feasible solution, the novel elitist search method is used to locally explore the search area around the fittest individual to find a better solution in its vicinity in genotype space represent by qubits. The solution of the unit commitment is carried out considering two small population sizes as suggested in earlier work by other authors using QIEA, though it can be extended using larger population size also. The computational time is also reduced by using the suggested method with a novel elitist local search (ELS) method. The results obtained after applying the proposed algorithm are found to better as compared to other well-known solution techniques.

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A review of different optimisation techniques for solving single and multi-objective optimisation problem in power system and mostly unit commitment problem

Cited by 10 publications

References 197 publications

Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization

Distribution Generators Optimal Power Flow with Renewable Resources Network with Grey Wolf Optimization

A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

Quantum Inspired Evolutionary Algorithm with a Novel Elitist Local Search Method for Scheduling of Thermal Units

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