Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

Yang, Yude; Wei, Bori; Liu, Hui; Zhang, Yiyi; Zhao, Junhui; Manla, Emad

doi:10.1109/access.2018.2865960

Cited by 40 publications

(25 citation statements)

References 69 publications

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“…For example the first difference of the data set D t is given by (29). By including the term d, finally the ARIMA(p,d,q) model in the terms of the lag operator is given by the (30).…”

Section: B Irradiance Forecasting Using Box Jenkins Methodologymentioning

confidence: 99%

“…The fireflies form the possible solution set of the given optimization problem and the dimensions of each firefly are dictated by the number of the decision variables of the objective function. The light intensity or the brightness of the fireflies depends upon the distance between the two fireflies [30]- [32] and is given by the inverse square law as follows:…”

Section: A Firefly Algorithmmentioning

confidence: 99%

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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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“…For example the first difference of the data set D t is given by (29). By including the term d, finally the ARIMA(p,d,q) model in the terms of the lag operator is given by the (30).…”

Section: B Irradiance Forecasting Using Box Jenkins Methodologymentioning

confidence: 99%

Section: A Firefly Algorithmmentioning

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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“…The firefly algorithm introduced in [27] to solve the economic dispatch of multi generation systems has poor global search mechanism which can result in the convergence of the algorithm towards local minimum. The firefly techniques introduced in [28], [29] consider the dynamic variation of the algorithm's parameters but the suggested techniques lack the exploration phase which can result in the premature convergence of the algorithm towards local optimum. The firefly technique discussed in [30] again uses the simple update criteria without considering the effect of the global best solution on the movement of remaining fireflies.…”

Section: B Research Gapmentioning

confidence: 99%

“…13) Repeat the steps (6)- (12) until the solution converges to the final value. 14) Find the total cost of the system using (29).…”

Section: ) Randomly Initialize the Volume Vectors As Fireflies Formentioning

confidence: 99%

Application of Dynamically Search Space Squeezed Modified Firefly Algorithm to a Novel Short Term Economic Dispatch of Multi-Generation Systems

et al. 2021

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“…This method works for the heat balance constraint by changing the generation and demand of power into heat. However, the slack method is effective only under the circumstance that compensation should be within a few generators because changing too many dimensions of a solution would damage the random search ability of SI algorithms [23]. Therefore, the number of slack units should be 1 by default, and only if 1 unit is not able to handle the slack value can we add more slack units.…”

Section: Algorithm 2 Polygon-handling Functionmentioning

confidence: 99%

A Heterogeneous Evolving Cuckoo Search Algorithm for Solving Large-Scale Combined Heat and Power Economic Dispatch Problems

Huang

Gao

et al. 2019

IEEE Access

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Combined heat and power economic dispatch (CHPED) problem aims to optimally schedule the output of generating units with minimum fuel cost, which is a highly non-linear, non-convex and large-scale global optimization problem with many practical constraints. The complexity of the problem demands solution methods with powerful search ability, robustness, and computational efficiency. This paper proposes a heterogeneous evolving cuckoo search (HECS) algorithm with a novel constraint-handling mechanism to solve the large-scale CHPED problem considering valve-point effect. Based on the cuckoo search algorithm, we apply a comprehensive learning strategy to enhance the search ability in the highdimensional environment, and a heterogeneous evolving strategy to improve the robustness of the algorithm. Moreover, we develop a novel constraint-handling mechanism that uses strict mathematical methods to repair unfeasible solutions and avoid redundant calculation. 5 tests are conducted on 24-unit, 48-unit, 84-unit, 96-unit, and 192-unit systems and the results are compared with the state-of-the-art algorithms published in the year 2015-2019. The comparisons show that the HECS could annually save millions of dollars in some large-scale systems, which verify its effectiveness. INDEX TERMS Combined heat and power economic dispatch, large-scale global optimization, valve-point effect, cuckoo search, constraint-handling mechanism.

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Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

Cited by 40 publications

References 69 publications

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

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

Application of Dynamically Search Space Squeezed Modified Firefly Algorithm to a Novel Short Term Economic Dispatch of Multi-Generation Systems

A Heterogeneous Evolving Cuckoo Search Algorithm for Solving Large-Scale Combined Heat and Power Economic Dispatch Problems

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