Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables

Liu, Bo; Li, Jiang; Ma, Haotian; Liu, Yiying

doi:10.1109/access.2020.3033224

Cited by 5 publications

(4 citation statements)

References 30 publications

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“…These optimization algorithm types are traditional mathematical algorithms or methods and metaheuristic algorithms. Numerous mathematical optimization methods including linear programming [10], linear/quadratic programming [11], sequential linear programming [12], newton method [13], generalized benders decomposition (GBD) [14], nonlinear programming [15][16][17], mixed integer nonlinear programming (MINLP), [18], interior point method [19,20], and simplified gradient method [21] have been applied to solve the OPF problems. In these traditional methods, nonlinear objective function and constraints are converted into linear form before solving the OPF problem because the mathematical method cannot handle the nonlinear properties of the problem [22].…”

Section: B Literature Reviewmentioning

confidence: 99%

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Javaid¹,

Niaz

Almogren

et al. 2021

IEEE Access

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In recent studies, emphasis has been placed on optimal power flow (OPF) problems in traditional thermal, wind, and solar energy sources-based hybrid power systems. Various metaheuristic algorithms have been proposed to find optimal solutions to the OPF problems in the hybrid power system. The OPF, due to the quadratic nature of its primary objective function, is a nonlinear, nonconvex, and quadratic optimization problem. In this study, we have proposed a bio-inspired bird swarm algorithm (BSA) to find an optimal solution to the OPF problem in the hybrid power system because it performs well in the case of optimizing the well-known Rastrigin quadratic benchmark function. In this study, uncertainty of utility load demand and stochastic electricity output from renewable energy resources (RESs) including wind and solar are incorporated into the hybrid power system for achieving accuracy in operations and planning of the system. We have used a modified IEEE-30 bus test system to verify and measure the performance of BSA and a comparison is made with well-known evolutionary metaheuristic algorithms. The proposed BSA consistently achieves more accurate and stable results than other metaheuristic algorithms. Simulation-based optimization results have shown the superiority of BSA approach to solve the OPF problems by satisfying all constraints and minimum power generation cost 863.121 $/h is achieved in case study 1. Simulation-based experiment results have indicated that by imposing the carbon tax (ton/h) the power generation from RESs was increased. In case study 2, the proposed BSA approach has also outperformed and minimum electricity cost 890.728 $/h is achieved as compared to other algorithms.INDEX TERMS Deterministic optimal power flow, Uncertainty of utility load demand, Bio-inspired bird swarm algorithm, Stochastic solar and wind power.

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Section: B Literature Reviewmentioning

confidence: 99%

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

Javaid¹,

Niaz

Almogren

et al. 2021

IEEE Access

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“…Successively, the topic has been highly investigated in many directions e.g. ill-conditioned systems [8,9], optimal power flow [10][11][12][13], AC/DC power flow with inclusion of HVDC-LCC and then HVDC-VSC [14][15][16]. However, these researches are always based on N-R and derived.…”

Section: Introductionmentioning

confidence: 99%

A Basic AC Power Flow Based on the Bus Admittance Matrix Incorporating Loads and Generators Including Slack Bus

Benato

2022

IEEE Trans. Power Syst.

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This paper presents an algorithm for solving the AC basic power flow based on some enrichments provided in the bus admittance matrix methods findable in the literature. In particular, the interpretation of the slack bus generator as a current source rather than a voltage one and its inclusion inside an "all-inclusive" admittance matrix allows obtaining strong performances of the algorithm. In fact, this method gives both a well conditioning of the admittance matrix and the reduction of matrix partitioning for each iteration. As a result, a greater precision of the solution, a shorter execution time compared to classical commercial methods, a decreasing number of iterations and optimal convergence properties are obtained. Eventually, in order to show the efficiency of the method, real and fictious networks are tested, by comparing its results and performances with robust open source/commercial software packages that use well-known methods (i.e. Newton-Raphson and Fast Decoupled Load Flow methods).

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“…The OPF problem is a nonlinear, nonconvex, and quadratic nature large-scale optimization problem. Initially, a numerous traditional mathematical approaches [2][3][4][5][6][7][8][9] are employed to address OPF problems. These mathematical methods include simplified gradient method [2], interior point method [3], mixed integer nonlinear programming (MINLP), [4], nonlinear programming [5], generalized benders decomposition (GBD) [6], newton method [7], linear programming [8], and linear/quadratic programming [9].…”

mentioning

confidence: 99%

“…Initially, a numerous traditional mathematical approaches [2][3][4][5][6][7][8][9] are employed to address OPF problems. These mathematical methods include simplified gradient method [2], interior point method [3], mixed integer nonlinear programming (MINLP), [4], nonlinear programming [5], generalized benders decomposition (GBD) [6], newton method [7], linear programming [8], and linear/quadratic programming [9]. The OPF problem objective functions and its imposed constraints are nonlinear in nature and the traditional mathematical methods could not solve it directly due to their linear approach [10].…”

mentioning

confidence: 99%

An Orthogonal Learning Bird Swarm Algorithm for Optimal Power Flow Problems

Javaid

Niaz

Ahmed³

et al. 2023

IEEE Access

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A dominant statistical method, in which the best combination of factors' levels are predicted by analyzing a few representative combinations of factors' levels named as orthogonal experimental design (OED). The OED is an effective approach for analyzing the effect of multi-levels factors simultaneously and it works on orthogonal learning (OL) strategy. An evolutionary programming based heuristic method has two contradictory features such as exploration and exploitation, balancing in these features have significant impact on its optimization performance. We have applied an OED based auxiliary search strategy for enhancing performance of the bird swarm algorithm (BSA) by improving its exploitation search ability. It is a challenging task to keep balance among two contradictory features -exploration and exploitation of a heuristic approach, while addressing optimal power flow (OPF) problems in power systems. In this research study, we have proposed improved BSA (IBSA) for solving the OPF problems in thermal power systems. We have conducted a study of the OPF problems with objective functions -reducing electricity generation cost, emission pollution, and active power loss to measure the efficiency of proposed IBSA. In this work, we have utilized five benchmark functions and solved OPF problems using three IEEE test systems including IEEE-30 bus system, IEEE-57 bus system, and IEEE-118 bus system to verify stability, effectiveness, and performance of proposed IBSA. The statistical and simulation results have indicated that the proposed IBSA has better convergence, efficiency, and robustness features than the original BSA as well as other heuristic approaches. It is observed that lowest electricity generation cost 800.3975$/h on IEEE-30 bus system, 41663.5500$/h on IEEE-57 bus system, and 134941.0367$/h on IEEE-118 bus system have been achieved using proposed IBSA to address the OPF problems. Furthermore, in transmission lines of the power system network minimum active power loss 16.2869MW has been observed by conducting a case study on the IEEE 118-bus system based on the proposed IBSA approach.INDEX TERMS Orthogonal learning, bird swarm algorithm, Optimal power flow, smart power grid I. INTRODUCTION A. BACKGROUND AND MOTIVATION

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Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables

Cited by 5 publications

References 30 publications

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

A Bio-Inspired Heuristic Algorithm for Solving Optimal Power Flow Problem in Hybrid Power System

A Basic AC Power Flow Based on the Bus Admittance Matrix Incorporating Loads and Generators Including Slack Bus

An Orthogonal Learning Bird Swarm Algorithm for Optimal Power Flow Problems

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