Optimal power flow based on parallel metaheuristics for graphics processing units

Roberge, Vincent; Tarbouchi, Mohammed; Okou, Francis A.

doi:10.1016/j.epsr.2016.06.006

Cited by 44 publications

(18 citation statements)

References 55 publications

(86 reference statements)

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“…As observing minimum cost of all methods, the proposed method can reach a better optimal solution than all methods with less cost by $168.91 to $9098.85, corresponding to the improvement from 0.13% to 6.55%. The reported values are not compared to GPUA-PSO because study [12] did not report control variables for checking. The mean cost and the worst cost of the proposed method are less than those of all methods.…”

Section: Results Comparisons For the Ieee 118-bus Transmission Power Nmentioning

confidence: 99%

“…Important parameters of loads are active power, reactive power, and working voltage limitations. Voltage limitations of loads are shown in Equation 10meanwhile the balances of active and reactive power are shown in Equations (12) and (13). Conductors or transmission lines are taken into account by the presence of apparent power limits shown in Equation 11.…”

Section: Constraints Of All Electric Componentsmentioning

confidence: 99%

“…In addition to DE variants, particle swarm optimization (PSO) variants were also successfully applied for the OPF problem considering different objectives and complicated constraints. These methods consist of PSO with chaos queues and self-adaptive mechanisms (IPSO) [9], evolving ant direction particle swarm optimization (EADPSO) [10], PSO with Pseudo-Gradient theory (PG-PSO) [11], graphics processing unit based PSO (GPUA-PSO) [12], and PSO with canonical differential evolution (CDE-PSO) [13]. In [9], the authors proved the outstanding performance of IPSO over conventional PSO through an IEEE 30-bus system considering multi-objective function.…”

Section: Introductionmentioning

confidence: 99%

“…Via the test results from benchmark functions and standard IEEE transmission power networks with 14, 30, 57, and 118 buses, the proposed method has been stated to be more effective than the conventional PSO. The studies in [12,13] have also tried to prove the real effectiveness of the applied methods by showing less fuel cost and less total power loss than other methods; however, the setting of iterations has not been considered for comparisons. In fact, the number of iterations was set to 3000 in [12] and 1000 in [13] for an IEEE 30-bus system whereas the settings for other remaining systems were not reported.…”

Section: Introductionmentioning

confidence: 99%

“…The studies in [12,13] have also tried to prove the real effectiveness of the applied methods by showing less fuel cost and less total power loss than other methods; however, the setting of iterations has not been considered for comparisons. In fact, the number of iterations was set to 3000 in [12] and 1000 in [13] for an IEEE 30-bus system whereas the settings for other remaining systems were not reported. Aside from DE and PSO variants, genetic algorithm (GA) variants were thoroughly exploited for the OPF problem by testing the conventional genetic algorithm and analyzing its shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

Cao

Đại

et al. 2019

Energies

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In the paper, a modified coyote optimization algorithm (MCOA) is proposed for finding highly effective solutions for the optimal power flow (OPF) problem. In the OPF problem, total active power losses in all transmission lines and total electric generation cost of all available thermal units are considered to be reduced as much as possible meanwhile all constraints of transmission power systems such as generation and voltage limits of generators, generation limits of capacitors, secondary voltage limits of transformers, and limit of transmission lines are required to be exactly satisfied. MCOA is an improved version of the original coyote optimization algorithm (OCOA) with two modifications in two new solution generation techniques and one modification in the solution exchange technique. As compared to OCOA, the proposed MCOA has high contributions as follows: (i) finding more promising optimal solutions with a faster manner, (ii) shortening computation steps, and (iii) reaching higher success rate. Three IEEE transmission power networks are used for comparing MCOA with OCOA and other existing conventional methods, improved versions of these conventional methods, and hybrid methods. About the constraint handling ability, the success rate of MCOA is, respectively, 100%, 96%, and 52% meanwhile those of OCOA is, respectively, 88%, 74%, and 16%. About the obtained solutions, the improvement level of MCOA over OCOA can be up to 30.21% whereas the improvement level over other existing methods is up to 43.88%. Furthermore, these two methods are also executed for determining the best location of a photovoltaic system (PVS) with rated power of 2.0 MW in an IEEE 30-bus system. As a result, MCOA can reduce fuel cost and power loss by 0.5% and 24.36%. Therefore, MCOA can be recommended to be a powerful method for optimal power flow study on transmission power networks with considering the presence of renewable energies.

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Section: Results Comparisons For the Ieee 118-bus Transmission Power Nmentioning

confidence: 99%

Section: Constraints Of All Electric Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

Cao

Đại

et al. 2019

Energies

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Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

Hassan

Elsayed

Kamel

et al. 2022

Intl J of Energy Research

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Summary During the last years, the electricity networks worldwide have rapidly developed, especially with integrating many types of renewable energy sources (RESs). The optimal operation is an opportunity to increase the penetration level of stochastic RESs into the power grid to maximize energy efficiency. Generally, the optimal power flow (OPF) problem is a highly complex, non‐convex, and non‐linear optimization problem. The complexity of the OPF problem is further increased as stochastic RESs are incorporated into the network. This paper presents an effective solution to the OPF problem for a traditional power generation with stochastic RESs. For solving this problem, chaotic Bonobo optimizer (CBO) is proposed in this paper based on the Chaos Theory to avoid the stuck in the local minimum by applying the original Bonobo optimization (BO). The performance of BO is enhanced using the chaotic maps sequences technique to enhance its global search capability and prevent getting stuck into local solutions. Uncertainty of the output power generated by RESs is forecasted based on probabilistic models. To minimize the total operating cost, the direct, underestimation, and overestimation costs of RESs are considered. Three different objective functions are considered, minimizing total operating cost, emissions, and power losses. Moreover, a carbon tax is incorporated in the objective function problem to minimize carbon emissions. The proposed OPF model and CBO technique are verified on the modified IEEE‐30 and IEEE‐57 bus test systems to confirm the superiority and effectiveness of the proposed CBO to achieve the optimal solution. The simulation results prove the efficiency and robustness of CBO for finding the best solution to the OPF problem with stochastic RESs.

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A novel multi-objective approach based on improved electromagnetism-like algorithm to solve optimal power flow problem considering the detailed model of thermal generators

Jeddi

Einaddin

Kazemzadeh

2016

Int. Trans. Electr. Energ. Syst.

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Summary This paper formulates a new multi‐objective model for the optimal power flow (OPF) problem considering 3 non‐commensurable and contradictory objectives, namely cost, emission, and loss. The proposed multi‐objective OPF problem takes a complete model for power generators including valve‐point effects, ramp rate limits, prohibited operating zones, spinning reserve, and multi‐fuel operation. Considering these constraints results in a non‐convex and non‐linear optimization problem and requires powerful methods to cope with. In this regard, the present paper proposes a multi‐objective electromagnetism‐like algorithm (EMA) based on the Pareto dominance concept and external archive strategy to solve this problem. The original EMA has poor search space exploration ability; hence, this paper proposes an improved EMA with modified local search process, which increases both exploration and intensification capabilities of the algorithm. The feasibility and effectiveness of the proposed method are tested on the IEEE 30‐ and 118‐bus test systems and the obtained numerical results are compared with the results of the original EMA and other heuristic methods reported in the recent literature.

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Optimal power flow based on parallel metaheuristics for graphics processing units

Cited by 44 publications

References 55 publications

Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

Optimal Power Flow for Transmission Power Networks Using a Novel Metaheuristic Algorithm

Developing chaotic Bonobo optimizer for optimal power flow analysis considering stochastic renewable energy resources

A novel multi-objective approach based on improved electromagnetism-like algorithm to solve optimal power flow problem considering the detailed model of thermal generators

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