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An Adaptive Modified Firefly Optimisation Algorithm based on Hong's Point Estimate Method to optimal operation management in a microgrid with consideration of uncertainties

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  • Mohammadi, Sirus
  • Mozafari, Babak
  • Solimani, Soodabeh
  • Niknam, Taher

Abstract

A probabilistic Energy Management system to optimize the operation of the Micro-Grid (MG) based on an efficient Point Estimate Method (PEM) is proposed in this paper. This method is used to model the uncertainty in the power generation of the wind farms and the Photovoltaic (PV) systems, the market prices and the load demands. PEMs constitute a remarkable tool to handle stochastic power system problems because good results can be achieved by using the same routines as those corresponding to deterministic problems, while keeping the computational burden low. For a system with m uncertain parameters, it uses 2m + 1 calculations of cost function to calculate the statistical moments of cost function solution distributions by weighting the value of the solution evaluated at 2m + 1 locations. The moments are then used in the probability distribution fitting. The normal, Beta and Weibull distributions are used to handle the uncertain input variables. Moreover, an Adaptive Modified Firefly Algorithm (AMFA) is employed to achieve an optimal operational planning with regard to cost minimization. Performance of the proposed method is verified using a typical MG.

Suggested Citation

  • Mohammadi, Sirus & Mozafari, Babak & Solimani, Soodabeh & Niknam, Taher, 2013. "An Adaptive Modified Firefly Optimisation Algorithm based on Hong's Point Estimate Method to optimal operation management in a microgrid with consideration of uncertainties," Energy, Elsevier, vol. 51(C), pages 339-348.
    • Handle: RePEc:eee:energy:v:51:y:2013:i:c:p:339-348
    DOI: 10.1016/j.energy.2012.12.013
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    References listed on IDEAS

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    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Roosta, Alireza & Amiri, Babak, 2012. "A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch," Energy, Elsevier, vol. 42(1), pages 530-545.
    2. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    3. Allen C. Miller, III & Thomas R. Rice, 1983. "Discrete Approximations of Probability Distributions," Management Science, INFORMS, vol. 29(3), pages 352-362, March.
    4. Shen, Yung-Chi & Chou, Chiyang James & Lin, Grace T.R., 2011. "The portfolio of renewable energy sources for achieving the three E policy goals," Energy, Elsevier, vol. 36(5), pages 2589-2598.
    5. Pantoš, Miloš, 2011. "Stochastic optimal charging of electric-drive vehicles with renewable energy," Energy, Elsevier, vol. 36(11), pages 6567-6576.
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