Solving of Optimal Power Flow Problem Including Renewable Energy Resources Using HEAP Optimization Algorithm

Shaheen, Mohamed A. M.; Hasanien, Hany M.; Al‐Durra, Ahmed

doi:10.1109/access.2021.3059665

Cited by 41 publications

(17 citation statements)

References 38 publications

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“…F1 neglects the cost of emissions, while another objective function (F2) is proposed with considering the cost of emissions to study its impact on the generation scheduling. F2 is expressed with the same cost models used in F1 in addition to the cost of emissions provided in (9) and (10).…”

Section: Objective Functions Of the Opfmentioning

confidence: 99%

“…In [9], a genetic algorithm, the two-point estimation method, and Monte Carlo simulation were applied to solve the OPF problem including RESs and an energy storage system, and the uncertain nature of wind, solar, and load demand was handled by a new strategy. In [10], the authors presented a novel heap optimization algorithm (HOA) to find the optimal solution of the power flow. HOA-based OPF methodology is flexible and applicable compared with that achieved by using the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

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ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

et al. 2022

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In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO2 emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO2 emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution.

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Section: Objective Functions Of the Opfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

et al. 2022

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“…In [21], a distributed PI controller to regulate a hybrid power system P&Q is presented. Subsequent, numerous optimization techniques, including particle swarm optimization (PSO) [22], Heap optimization algorithm (HOA) [23], genetic algorithm (GA) [24], sunflower optimization algorithm (SFO) [25], hybrid firefly and particle swarm optimization technique [26], Salp swarm algorithm [27], hybrid GWO-PSO optimization technique [28], hybrid cuckoo search algorithm and grey wolf optimizer (CSA-GWO) [29], equilibrium optimization algorithm (EO) [30], and Whale Optimization Algorithm (WOA) [31], have been used in the MG to enhance decentralized controllers. As reported in [32], these approaches have however advantages and disadvantages, being still so far to get a universal framework for MG control.…”

Section: B Research Gap and Motivationmentioning

confidence: 99%

Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation

et al. 2022

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This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system. INDEX TERMS distributed generators; sunflower optimization algorithm; microgrid; renewable energy; coot bird metaheuristic optimizer.

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“…The real-time and accuracy of the next-time user power consumption forecast is not only one of the realization goals of demand-side management, but also has positive significance for the safe and stable operation of the power system and the improvement of economic benefits. Due to the characteristics of randomness and uncertainty in short-term power consumption of users, the choice of forecasting method and its ability to overcome the randomness directly affect the implementation quality of real-time and accurate power dispatch [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Disturbance Evaluation in Power System Based on Machine Learning

Sivakumar¹,

Ibrahim²

2022

Computers, Materials &Amp; Continua

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The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.

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Solving of Optimal Power Flow Problem Including Renewable Energy Resources Using HEAP Optimization Algorithm

Cited by 41 publications

References 38 publications

ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation

Disturbance Evaluation in Power System Based on Machine Learning

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