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The main aims of this paper are to 1) predict the uncertainties using the hybrid method of WT-ANN-ICA and 2) determine the optimal generation strategy of a micro-grid (MG) containing wind farms (WFs), photovoltaic (PV), fuel cell (FC), combined heat and power(CHP) units, tidal steam turbine (TST), and also boiler and energy storage devices (ESDs). The scenario-based stochastic optimization problem is presented to determine the optimal points for the energy resources generation and to maximize the expected profit considering demand response (DR) programs and uncertainties. The uncertainties include wind speed, tidal steam speed, photovoltaic power generation (PVPG), market price, power and thermal load demand. For modeling uncertainties, an effort has been made to predict uncertainties using the hybrid method of wavelet transform (WT) in order to reduce fluctuations in the input historical data. An improved artificial neural network (ANN) based on the nonlinear structure is also used for better training and learning. Furthermore, the imperialist competitive algorithm (ICA) is adopted to find the best weights and biases for minimizing the mean square error of predictions. In the present study, three cases are investigated to confirm the performance of the proposed method. The first case study is programing MG isolated from grid, the second and the third case studies respectively are pertaining to comparison of the prediction of uncertainties using WT-ANN-ICA and WT-ANN methods, and effect of DR programs on the expected profit of energy resources in grid-connected mode.
Jafari, E., Soleymani, S., & Mozafari, B. (2018). Scenario-based stochastic optimal operation of wind/ PV/FC/CHP/boiler/tidal/ energy storage system considering DR programs and uncertainties. WSEAS Transactions on Power Systems, 13, 386–398. https://doi.org/10.1186/s13705-017-0142-z