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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

Mohammadi, Sirus, Mozafari, Babak, Solimani, Soodabeh, Niknam, Taher
Energy 2013 v.51 pp. 339-348
algorithms, energy, market prices, model uncertainty, planning, power generation, probability distribution
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.