Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

Ilyas, Muhammad Arsalan; Abbas, Ghulam; Alquthami, Thamer; Awais, Muhammad; Rasheed, Muhammad Babar

doi:10.1109/access.2020.3014046

Cited by 23 publications

(10 citation statements)

References 68 publications

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“…These methods are extremely capable to deal with the nonlinear and complex problems of the power system. [24]. They are adaptable and can return several solutions to a single problem in a single simulation run.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

et al. 2022

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This paper deals with the combined economic emission load dispatch (CEELD) problem with and without the integration of renewable energy sources (RESs), in some more rational test scenarios of single CEELD and multi-objective CEELD (MO-CEELD) optimization. Hence, an efficient and coherent approach is presented to minimize the generation and emission cost using one of the bio-inspired metaheuristic algorithms named flower pollination algorithm (FPA). The evolution of a power system along with the integration of RESs demands equal advancement in the operation and control algorithms of the power grid. Therefore, the proposed approach in this paper offers an evolutionary single and multiobjective optimization process based on a bio-inspired FPA. Further, it has been validated by achieving the best compromise solution (BCS) using the Pareto categorizing process and fuzzy membership function. Moreover, different study cases comprising eleven and fifteen thermal units with and without considering RESs are tested with the proposed technique. Finally, the effectiveness of the proposed approach is tested by comparing the simulation results with some already existing techniques in terms of overall fuel and emission cost. Significantly, it has been noticed from the results that it outperforms all the previously presented approaches like PSO, DE, GSA, AEO, BA, and dBA, thus justifying its applicability.INDEX TERMS Combined economic emission dispatch, fuzzy membership function, multi-objective flower pollination algorithm, Pareto categorizing process, price penalty factor approach.

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Section: Literature Reviewmentioning

confidence: 99%

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

et al. 2022

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“…In the last decade, some studies also have been documented [43][44][45][46][47][48][49][50][51][52][53] to find optimal solution to the OPF problems in the thermal, wind, and solar energy sources-based hybrid power systems. In recent studies [43][44][45][46][47][48][49][50][51][52][53], different metaheuristic approaches including grey wolf optimizer (GWO) [43], fuzzy membership function based PSO (FMF-PSO) [44], improved adaptive DE (IADE) [45], modified imperialist competitive algorithm based on sequential quadratic programming (MICA-SQP) [46], modified JAYA [47], hybrid of phasor PSO and GSA [48], barnacles mating optimization (BMO) [49], PSO [50], Hybrid of DE and PSO [51], MBFA [52], and sunflower optimization (SFO) [53] have been proposed for solving the OPF problems in hybrid power systems.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In studies [43][44][45][46][47][48][49][50][51][52][53], mostly Lognormal PDF and Weibull PDF have been applied for modeling uncertainty of the stochastic solar irradiance and wind speed, respectively.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Table 2 represents the summary of all of the aforementioned studies [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] have been conducted on thermal and wind or thermal, wind, and solar energy sources-based hybrid power systems. As specified in Table 2, OPF problem primary objective -quadratic fuel cost ξ q (electricity generation cost) minimization is considered in all studies.…”

Section: B Literature Reviewmentioning

confidence: 99%

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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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“…Therefore, generation cost is kept at its minimum value along with the reduction in line loss of the system. The FMF values μ for RES contributions are computed and defined as in Ilyas et al (2020a) by the following equation:…”

Section: T-fmfmentioning

confidence: 99%

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

et al. 2021

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The performance of a power system can be measured and evaluated by its power flow analysis. Along with the penetration of renewable energies such as wind and solar, the power flow problem has become a complex optimization problem. In addition to this, constraint handling is another challenging task of this problem. The main critical problem of this dynamic power system having such variable energy sources is the intermittency of these VESs and complexity of constraint handling for a real-time optimal power flow (RT-OPF) problem. Therefore, optimal scheduling of generation sources with constraint satisfaction is the main goal of this study. Hence, a renewable energy forecasting–based, day-ahead dynamic optimal power flow (DA-DOPF) is presented in this paper with the forecasting of solar and wind patterns by using artificial neural networks. Moreover, contribution factors are calculated using triangular fuzzy membership function (T-FMF) in the sub-interval time slots. Furthermore, the superiority of feasible (SF) solution constraint handling approach is used to avoid the constraint violation of inequality constraints of optimal power flow. The IEEE 30-bus transmission network has been amended to integrate a solar photovoltaic and wind farm in different buses. In this approach, the computing program is based on MATPOWER which is a tool of MATLAB for load flow analysis which uses the Newton–Raphson technique because of its rapid convergence. Meteorological information has been gathered during the time frame January 1, 2015, to December 31, 2017, from Danyore Weather Station (DWS) at Hunza, Pakistan. A Levenberg–Marquardt calculation–based artificial neural network model is utilized to foresee the breeze speed and sunlight-based irradiance in light of its versatile nature. Finally, the results are discussed analytically to select the best generation schedule and control variable values.

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Multi-Objective Optimal Power Flow With Integration of Renewable Energy Sources Using Fuzzy Membership Function

Cited by 23 publications

References 68 publications

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

Economic Emission and Energy Scheduling for Renewable Rich Network Using Bio-Inspired Optimization

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

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

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