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• W2895767766 abstract "Ever-increasing rate of wind power generation as an uncertain and variable source of energy, face new challenges in power system operation. For this reason, optimal real-time operation of wind integrated power systems in profit based markets considering the effects of probabilistic future variations of wind speed is one of the main concerns of system operators. For this purpose, some solutions such as demand response (DR) programs and energy storage systems (ESS) are widely being used to cover and manage wind generation uncertainty. But some of them such as DR programs can add some extra sources of uncertainty due to unpredictable customer’s behavior. To this end, this paper proposes an online model-based predictive control approach for optimal real-time operation of wind integrated power systems including DR and ESS facilities. Discrete-time manner, re-optimization characteristic, and adaptability are the main features of the proposed MPC method which make it well-suited to address high uncertainties regarding wind power generation and customer’s behavior. Besides, MPC considers all interactive effects of the control facilities in accordance with the expected wind farm output power in the future prediction horizon to maximize wind power utilization and so enhance social welfare. In addition, the uncertain nature of wind power is modeled using Markov chain Monte Carlo method. For efficiency evaluation of the proposed approach, simulation is implemented in MATLAB software using YALMIP optimization toolbox for the 8-bus test system. Results confirm the acceptable performance of the proposed approach in reducing operation cost through optimal uncertainties management." @default.
• W2895767766 created "2018-10-12" @default.
• W2895767766 creator A5033257604 @default.
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• W2895767766 date "2019-03-01" @default.
• W2895767766 modified "2023-10-01" @default.
• W2895767766 title "MPC-based approach for online demand side and storage system management in market based wind integrated power systems" @default.
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• W2895767766 doi "https://doi.org/10.1016/j.ijepes.2018.09.041" @default.
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