Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems

Ghosh, Swaroop; Kaur, Manvir; Bhullar, Suman; Karar, Vinod

doi:10.3390/en12030551

Cited by 27 publications

(12 citation statements)

References 29 publications

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“…In the second iteration, the best positions selected are mutated separately by applying different steps for both algorithms and the cost is therefore calculated. More details about these hybrid methods are found in [55] and [56]. In the present work, these hybrid algorithms are evaluated using the IEEE-30 and 57 bus systems.…”

Section: Case Studies and Results Using Hybrid Algorithmsmentioning

confidence: 99%

Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources

et al. 2020

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Today's electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWObased approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.

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Section: Case Studies and Results Using Hybrid Algorithmsmentioning

confidence: 99%

Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources

et al. 2020

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“…The author compares the performance of the CSA with the genetic algorithm and concludes that CSA outperforms the genetic algorithms. The work in [15] uses the hybrid abc-bat algorithm for finding the optimal solution of the proposed scheduling problem and compares its performance with the other hybrid techniques like hybrid PSO. Similarly, the references [16]- [18] discuss the hydrothermal scheduling problem using different techniques while considering the multiple generation constraints.…”

Section: B Literature Reviewmentioning

confidence: 99%

Performance Analysis of APSO and Firefly Algorithm for Short Term Optimal Scheduling of Multi-Generation Hybrid Energy System

et al. 2020

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This paper presents a modified and novel form of the conventional short-term hydrothermal scheduling problem by incorporating the effects of adding the photovoltaic energy source to the conventional grid. A photovoltaic energy source is intermittent in nature, therefore, to determine the optimal power contribution of the photovoltaic source towards the economic dispatch problem, a detailed strategy is presented in this paper. The proposed design method includes the forecasting of the photovoltaic system's parameters using the Auto-Regressive Integrated Moving Average (ARIMA) model. The analytical model is developed based on the fractional integral polynomials for studying the characteristics of the single photovoltaic module. The optimization of power allocation in the system consisting of conventional and non-conventional energy sources is highly non-linear and classical deterministic methods can not be guaranteed to determine the optimal solution for economic power dispatch. The global optima of such non-linear and non-convex problems can be determined using swarm-based intelligence techniques. This paper presents accelerated particle swarm optimization and the firefly algorithm to determine a solution for short-term non-linear scheduling problems. The multiple test cases have been presented to demonstrate the effectiveness of the proposed solution over classical methods. The overall generation cost of the selected hybrid system is reduced using the proposed methods while meeting the generation constraints of each energy source. Moreover, due to the stochastic nature of the meta-heuristic techniques, a comprehensive statistical comparison, based on the independent T-test results, is also presented to highlight the algorithm which performs better for selected scheduling problems. It has been demonstrated that accelerated particle swarm optimization gives lower mean generation cost of the system whereas the execution time of the firefly algorithm is better compared to its counterpart. INDEX TERMS Mathematical modeling, auto-regressive integrated moving average model, accelerated particle swarm optimization, firefly algorithm, short term hydrothermal scheduling, independent t-test.

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“…Hybrid algorithms combine the best features of two or more approaches, by replacing the weakness of one approach with advantages (strengths) of some other approach. By examining available literature sources, it can be seen that hybrid algorithms can be very efficient in tackling different types of problems [85][86][87]. Moreover, hybrids between different categories of artificial intelligence (AI) algorithms also exist.…”

Section: Proposed Hybrid Whale Optimization Algorithmmentioning

confidence: 99%

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

et al. 2019

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The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation-exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

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Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems

Cited by 27 publications

References 29 publications

Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources

Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources

Performance Analysis of APSO and Firefly Algorithm for Short Term Optimal Scheduling of Multi-Generation Hybrid Energy System

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

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