Abstract
This chapter investigates an optimization based on the model predictive control (MPC) of a hybrid power system containing a photovoltaic (PV) system, wind turbine (WT), and battery storage system connected to the national grid. The study also develops a strategy able to minimize the energy cost of a household application. A time-of-use tariff is applied in the energy management strategy by proposing a novel residential feed-in tariff incentive in Morocco as a case study to earn more cost–benefit. A linear programming algorithm is designed to reduce the net energy cost, which is formulated as two function costs, the cost of purchasing electricity from the utility grid and the grid-sell back. The energy sold to the grid is the excess energy from PV, WT, and battery bank, which is controlled by taking into consideration the electricity price periods and the dynamic state of charge of the batteries. Then, an MPC design is implemented based on a multiinput–multioutput state-space model to make the system robust against the external disturbances occurring on renewable energy sources or the household demand. A comparison between the two proposed strategies is performed in terms of energy and cost-savings as well as the baseline cost for which the hybrid system operates without the interaction of the control optimal. Different simulation tests are carried out and discussed to demonstrate the robustness and effectiveness of the proposed MPC strategy. Thanks to the ability of the MPC to define the appropriate control and predict the future system response, the adopted strategy guarantees great daily energy and cost-savings.
Published Version
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